MASTER'S THESIS
Tones In Noise
A study of the perception of motor noise in an electric vehicle
Thomas Lindbom
Master of Science in Engineering Technology
Arena media, music and technology
Luleå University of Technology
Department of Business administration, Technology and Social science
Tones in Noise
-A study of the perception of motor noise in an electric vehicle
Thomas Lindbom
Master of Science Thesis
Arena Media Music and Technology
Abstract
Electric and hybrid vehicles are becoming a more and more frequent addition in car
manufacturers’ fleets of vehicles. Noise and vibrations in these vehicles differ from the noise
and vibrations produced in a combustion engine car. Sound quality of combustion engines
have previously been researched but little information is to be found on the sound quality of
electric vehicles. In this study the perception of the tonal components produced by the
electric motor in a pure electric car is investigated. The tones were rated on annoyance and
well sounding scales by 30 subjects in a listening test and the threshold of when the tones
become audible was investigated. Coupled to the subjects’ ratings is a measure called
prominence ratio. Prominence ratio is used in the IT industry to classify the prominence of
tonal components in IT-products. Sound files used in the listening test were recorded in a
Volvo C30 electric on smooth and rough asphalt at constant speeds of 50 km/h, 80 km/h
and 0-100 km/h acceleration. The recorded files were filtered to suppress existing tonal
components. Synthesized tones, created to represent the tones produced by the electric
motor, were added to the filtered sound files. The synthesized tones were generated in two
programs, either Pure Data or Head Acoustics Artemis. The results from the listening test
show that the presence of tones in the interior sound of a car leads to an increase in
annoyance compared to when the tones are not heard. In the study, tones below 1 kHz
were harder to detect than tones above 1 kHz. The low frequency tones were detected by
more than 50% of the subjects at values of prominence ratio between 1-4 dB and the higher
tones were detected at 0 dB. The results also reveal that frequencies above 1 kHz were
rated as more annoying than tones below at a significance level of 95%.
Sammanfattning
El- och Hybridbilar blir ett allt vanligare tillskott i dagens biltillverkares flottor. Dessa bilars
ljud- och vibrationsegenskaper skiljer sig åt gentemot bilar med förbränningsmotorer. Vad
som kännetecknar ljudkvalité för en bil med förbränningsmotor har tidigare undersökts i
ett flertal studier. Vad som kännetecknar ljudkvalité i en elbil är dock ett mycket mindre
utforskat område. Denna studie har undersökt upplevelsen av de tonala komponenter som
skapas av en elmotor i en elbil. Hur störande och hur välljudande tonerna var bedömdes av
30 personer i ett lyssningstest. I lyssningstestet undersöktes även vid vilken nivå över
bakgrundsbruset dessa toner blev hörbara. Subjektiva bedömningar har kopplats till ett
psykoakustiskt mått som heter prominence ratio. Detta mått är framtaget för att användas i
IT-industrin för att klassificera tonala komponenter i IT-produkter. I lyssningstestet
användes ljudfiler inspelade med ett konsthuvud inuti en Volvo C30 electric på slät och grov
asfalt, vid 50 km/h och 80 km/h konstant fart samt en 0-100 km/h acceleration. Ljudfilerna
har filtrerats på ett sådant vis att befintliga tonala komponenter har undertryckts. Till de
filtrerade ljudfilerna har sedan syntetiserade toner, skapade i Pure Data eller Artemis,
adderats. Resultatet från lysningstestet visar att hörbara toner från elmotorn inuti bilen
leder till ett mer störande ljud än om tonerna ej hade hörts. Studien visar också att toner
under 1 kHz är svårare att uppfatta än högre toner. De lägre tonerna uppfattas av mer än
50% av försöksdeltagarna vid en prominence ratio på 1-4 dB medan de högre tonerna
uppfattas vid en prominence ratio på 0 dB. Resultat ifrån lyssningstestet visar med 95%
konfidensnivå att de toner som var högre än 1 kHz var mer störande än de toner som var
lägre än 1 kHz.
Preface
This is a thesis for a Master of Science education called Arena Media Music and Technology
at Luleå University of Technology. The thesis work was carried out at the Noise, Vibration
and Harshness department of Volvo Car Corporation in Göteborg.
First of all I would like to thank my supervisor David Lennström at Volvo for giving me a
great introduction into the world of automotive NVH. In addition to this he has also been
very open for discussions which have hatched some great ideas used throughout the thesis.
Secondly I would like to thank Arne Nykänen, my academic supervisor, for very good
pointers about the methodology and the work in general.
I would also like to thank Patrik Johansson, Fredrik Hagman and Anders Sköld for their help
in answering all sorts of questions concerning NVH.
Last but not least my thanks go out to my family and friends and especially Emma Helldner
for your never-ending support.
Göteborg, December 2012.
Thomas Lindbom
Contents
1. Introduction ..................................................................................................................... 1
1.1 Background ............................................................................................................... 1
1.2 Scope ......................................................................................................................... 2
1.3 Goals and research questions .................................................................................... 2
1.4 Limitations ................................................................................................................ 2
2. Theoretical background .................................................................................................. 3
2.1 Psychoacoustics ........................................................................................................ 3
2.2 Psychoacoustic metrics ............................................................................................. 4
2.3 Sound quality- and character in cars ......................................................................... 7
2.4 Interior sound in cars ................................................................................................ 7
2.5 The electric vehicle ................................................................................................... 8
2.6 Electric motor acoustics ............................................................................................ 9
2.7 Statistics .................................................................................................................. 10
3. Method .......................................................................................................................... 12
3.1 Recording ................................................................................................................ 12
3.2 Suppressing tones with filters ................................................................................. 13
3.3 Create sinusoidal tones with Pure Data and Artemis .............................................. 19
3.4 Add tones to filtered material ................................................................................. 22
3.5 The sound files ........................................................................................................ 25
3.6 Evaluate with a listening test .................................................................................. 26
4. Results and Discussion ................................................................................................. 30
4.1 The threshold of audibility ...................................................................................... 30
4.2 Conclusion: Research question 1. ........................................................................... 33
4.3 Analysis of annoyance ............................................................................................ 34
4.4 Conclusion: Research question 2 ............................................................................ 43
4.5 Analysis of well sounding ....................................................................................... 44
4.6 Tone in 1/3-octave band.......................................................................................... 46
4.7 Third and fourth part of the listening test - Subjective ratings ............................... 47
4.6 Conclusion: Research question 3 ............................................................................ 48
4.7 Conclusion: Research question 4 ............................................................................ 48
5. Conclusions ................................................................................................................... 49
6. Further work.................................................................................................................. 50
7. References ..................................................................................................................... 51
8. Appendix ....................................................................................................................... 53
7.1 Appendix 1: Listening test introduction text (Swedish) ......................................... 53
7.2 Appendix 2: Listening test introduction text (English)........................................... 53
1. Introduction
1.1 Background
Controlling noise and vibration in vehicles is very important. Not only is there a need to
fulfill legislations on exterior noise to achieve low noise emissions from road and power
train but there is also a need for good sound quality inside the vehicle. If a car manufacturer
wants to be considered as a premium brand interior noise is a vital aspect to fulfill
customers’ expectations. Poor sound quality in areas such as road, power train and wind
noise as well as ill-sounding components for example doors and windshield wipers may
lead to fewer sold vehicles. There is also another important category of interior noise
namely engine and driveline noise. How the engine of a vehicle sound is a very important
audible cue for which type of car it is and in which condition the car is in. Engine sound can
also be used as a trademark for a certain kind of vehicles, e.g. high performance sports cars
or a Harley Davidson motorcycle.
In over a hundred years of automotion the dominant type of engine has become the internal
combustion engine but with an increasing customer demand for cars with low emissions of
CO2 car manufacturers all over the world now investigate the use of alternative propellants
in new vehicles. Such vehicles can be pure electric vehicles or a combination of an internal
combustion engine and an electric motor in so called hybrid vehicles. The pure electric car
is only run on the energy stored in batteries which drive an electric motor. The car itself
produces no emissions in terms of greenhouse gases and thus fulfilling customer demand
for low CO2 emissions. With the change from an internal combustion engine to an electric
motor, noise and vibration engineers are faced with new problems. There is no longer any
broadband noise from the internal combustion engine which hides unwanted noise from
pumps, relays, fans and other components. The car is very quiet at low speeds and can be
hard to detect for pedestrians. At standstill it can be hard for the driver to know if the car is
started or not due to no idling noise and vibrations. Another important aspect is the motor
sound itself. It differs a great deal from an internal combustion engine. It is quieter, has a
more tonal character and contains more high frequencies. The preferences of motor sound
from an electric vehicle are not well known and need to be investigated. This thesis explores
how the tones from the motor are experienced in the passenger compartment.
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1.2 Scope
The subjective impression of the tonal components generated by an electric motor is
investigated in this thesis. The presence of these tones is thought to be a source of
annoyance and if they are, the frequency dependence of annoyance is of interest. Another
aspect investigated is if the presence of tones in some frequencies can have a positive
contribution to perceived sound quality. The work is based on recordings from two
different road surfaces and constant speed as well as accelerating driving conditions to
validate if impressions are similar.
1.3 Goals and research questions
The main goal of this thesis is:

Conduct an experiment to determine the level where annoyance is affected by tonal
components, expressed in prominence ratio or tone-to-noise ratio as a function of
frequency.
To be able to achieve the main goal the following is investigated:
1. Find values of prominence ratio where tones become audible.
2. Find out if tones from the electric motor are perceived as annoying and if so, are all
levels and frequencies equally annoying?
3. Is there any point in having electric motor noise inside the passenger compartment?
Does some tonal content provide clues about the vehicle performance?
4. Are there similarities in the perception of the motor noise on different road surfaces,
different driving speeds and driving conditions?
1.4 Limitations
The tones generated by the electric motor contain a large amount of harmonics. This means
the tones are multiples of a fundamental frequency. The perception of multiple tones, from
the electric motor, present simultaneously is not investigated. This thesis handles one tone
at the time resulting in easier control over specific frequencies or frequency ranges. The
thesis does not handle tones that vary in amplitude over time. Such tones can be heard in an
accelerating driving case. Neither does the thesis handle how the DC/AC inverter PWM
switching frequency is perceived. The switch frequency has potential to be a source of
annoyance because it does not follow the rpm of the engine. Sound files used in the listening
test were recorded on two different surfaces, in two different speeds and also a zero to 100
km/h acceleration. There was only one car used for all recordings. The results should
however not be limited to be valid for that car only because it was the tones that were
under investigation and not the sound quality of the specific vehicle.
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2. Theoretical background
2.1 Psychoacoustics
The perception of sound and noise is a complicated matter. There are psychological aspects
of people’s relation to the sound or noise they are currently exposed to as well as effects
related to how the human auditory system works. Some phenomena caused by the human
auditory system are described by Hugo Fastl and Eberhard Zwicker in Psychoacoustics:
Facts and Models [1]. They mention that a knowledge of masking, critical bands and pitch is
important when doing work that involves why sound is perceived the way it is.
Masking
The masking of sound is something humans deal with every day. This effect can be observed
when a clearly audible tone becomes inaudible in the presence of another tone or noise in
the same critical band. A short tone can even be masked by events before or after the tone is
played. The strongest masking effect occurs when the masking noise is played before the
tone. Masking of various degrees is possible, called partial masking. The point in which a
tone masked by noise is inaudible is called the masked threshold.
Critical band
When a tone is presented together with a broad band noise only a part of the noise is
masking the tone. The range of the noise, effectively masking the tone, is a frequency range
called critical band. For frequencies below 500 Hz critical bands can be considered to be
100 Hz wide and for frequencies above 500 Hz the width is about 0,2 times the center
frequency. Using these rules the critical-band rate scale, with unit Bark, can be produced.
Pitch and Virtual Pitch
A subjective description of sound and frequency can be measured in Mel and is called pitch.
It is a function of frequency and it also depends on sound-pressure level (SPL). For pure
tones increasing sound pressure levels leads to an increase of pitch for high frequencies and
a decrease in pitch for low frequencies. Complex sound with a harmonic set of partials is
usually interpreted as one prominent pitch. The prominent pitch is normally the difference
between two adjacent harmonics. The psychoacoustic phenomena of virtual pitch can occur
when a harmonic tone series lack its fundamental frequency. Even though the fundamental
is missing the prominent pitch is still the fundamental frequency, hence the name Virtual
Pitch.
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2.2 Psychoacoustic metrics
In order to be able to objectively measure sound character, psychoacoustic metrics have
been developed. These metrics enables the quantification of certain aspects of a sound and
take into account the non-linearity of the human auditory system.
Loudness [2]
The perceived loudness, measured in phon, of sound cannot be judged purely on its
intensity. Several effects are present e.g. frequency dependence, bandwidth, time variations
and sound-pressure level. Phon is a value of the physical loudness and relates to a certain
Sound Pressure Level (SPL) at 1 kHz. A subjective unit of loudness is the term sone which
uses the fact that the human hearing interprets an increase in SPL by 10 dB as twice as high.
This correlates to a doubled value in sones. The same relation is also true for a SPL decrease
of 10 dB leading to half the original sone value. One sone is equal to a sound-pressure level
of 40 phons at a frequency of 1 kHz. If the sone value is doubled or halved the phon value is
increased or decreased by 10 dB. There is a significant difference in the loudness of a pure
tone or a broadband noise for an equal SPL. The loudness of the broadband noise is greater.
Loudness for noise also depends on the width of critical bands.
The frequency dependence of perceived loudness is visualized in the equal loudness
contours in Figure 1. The decade numbers mark the level of a perceived sine-tone at 1 kHz.
The curves represent how the sound pressure level of a sine tone needs to be changed in
order for it to be perceived equally loud throughout the frequency range. If for example a
sine is heard at a SPL of 40 dB at 1000 Hz it needs to be played at a higher SPL-value at for
example 50 Hz for it to be perceived as loud as when it was played at 1000 Hz. The fact that
the ear is most sensitive at around 3-5 kHz is because of resonances in the ear canal
increasing the SPL. In Figure 1 the lower threshold of hearing is also shown.
Figure 1: Equal loudness contours [1].
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Sharpness
A quantifiable measurement of sound is sharpness and its unit is acum. One acum
corresponds to a one critical band wide noise of 60 dB SPL at the center frequency 1 kHz.
Sharpness can be calculated and the frequency relation is governed by a weighting function
which produces higher values of Sharpness if there is more high frequency content.
Sharpness can therefore be used to describe high frequency content and increases with
purer tones at higher frequencies [1].
Fluctuation strength and Roughness
If two pure tones, closer than 15 Hz in frequency, are played simultaneously amplitude
variations can be heard. It reaches a maximum at 4 Hz difference between tones and the
unit of fluctuation strength is Vacil. In close relation to this phenomenon is roughness,
measured in Asper. It appears when frequency differences are in the range of 15-300 Hz
and has a maximum at a 70 Hz difference. A difference between fluctuation strength and
roughness is that a fluctuating sound is perceived to have variations in amplitude whilst a
rough sound is perceived at a constant level [1].
Tonalness
If a sound has the ability to produce a sensation of pitch or contains prominent tones it can
be considered tonal. The opposite of such a sound would be white noise. The perception of
tonalness in noise is affected by a number of things. In a study by Vorman et al. the
frequency dependence of perceived tonalness is described. The conclusion drawn was;
whether it is a single tone, a tone partially masked by noise or a tone at the masked
threshold the sound was considered most tonal at frequencies between 920 Hz and 1450 Hz
[3]. The fact that tonalness depends on frequency is also supported in another study by
Vormann et. a.l. which also states that an increase in the number of harmonics (in this case
two, four or eight) produces a more tonal sound compared to a non-harmonic sound [4]. A
study with synthesized interior high speed train noise was conducted by Patsouras et al. In
their study they either increased or decreased the 630 Hz or 1250 Hz 1/3 octave band one
at the time. Their results showed that a tonal sensation was achieved in both cases, increase
or decrease, although the tonal sensation was rated to be much higher when the 1/3 octave
band was increased in SPL [5]. Tonalness can be a root to increased annoyance of noise in
many areas, for example interior noise of cars, aircraft noise and the interior noise of high
speed trains [5, 6, 7, 8]. Studies suggest that increasing tonalness leads to higher rated
annoyance and a decreased sensation of quality [5, 8, 9, 10].
The detection of tones by signal processing is not simplistic and depends on relative levels
of the tones, number of tones, distance in frequency between tones and the noise or sound
in which the tones reside. Two methods for calculating the prominence of a tone are the
tone to noise ratio (TNR) and prominence ratio (PR) described in ECMA-74 [11]. The ECMA74 international standard provides information of how to conduct measurements of noise
emission from information technology and telecommunication devices. In annex D the
tonalness of the noise emission is taken into consideration. Using two different methods,
tone to noise ratio (TNR) and prominence ratio (PR), a discrete tone can be classified as
prominent or not. Both classification methods are frequency dependent and are capable of
5
dealing with discrete tones ranging between 89,1 Hz and 11220 Hz. Tones with a higher
frequency than 11220 Hz can also be used but there is no psychoacoustical data providing
limits for prominence. The limits for when a tone is considered prominent, using either of
the methods and their frequency dependency, is shown in Figure 2. TNR and PR use Fast
Fourier Transform (FFT) for calculating prominence values. The FFT should be set to no
frequency weighting, Hanning windowing and linear averaging. The tones should preferably
be narrower than 15% of the width of the critical band in which they are centered.
Figure 2: Criteria for prominent tones according to tone-to-noise ratio and prominence ratio
as a function of frequency [11].
Tone to noise ratio
The difference in the mean-square sound pressure or sound pressure level (SPL) of a
discrete tone and the masking noise of the critical band centered on the tone is the
definition of the tone to noise. The masking noise does not include the sound pressure or
SPL of the tone itself.
If multiple tones are included in the same critical band each tone is either treated
individually or combined when a criterion of proximity is met. If the tones are of a harmonic
nature i.e. multiples of a fundamental frequency each tone can be treated individually. The
tone to noise ratio is better suited for dealing with tones of a harmonic nature than multiple
tones within a critical band.
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Prominence ratio
The prominence ratio uses data from three critical bands, one centered on the frequency of
the tone and one on either side. The difference between the mean-square sound pressure
and SPL of the critical band centered on a tone and the mean-square sound pressure or SPL
of the two adjacent critical bands is called the prominence ratio. When the prominence ratio
is used for tones of a harmonic nature each tone should be dealt with individually.
2.3 Sound quality- and character in cars
In order for a car to be categorized in the premium segment it is important to have good
Product Sound Quality (PSQ). PSQ is affected by sensations, previous experiences and
expectations relating to the product. For a car, this means that sounds such as squeaks,
rattles or a hollow door-closing sound directly affect PSQ in a negative way. A further
important part of the premium package should also be a brand specific sound. A low Aweighted SPL alone isn’t enough to achieve a high market position and this indicates the
importance of sound character [12]. Sound character can be described by psychoacoustic
properties for example sharpness and roughness and should have no underlying
correspondence with personal opinions regarding the sound.
Lee et al. found in a study of seven luxury cars that 50% of the sound quality depends on
engine noise and that during acceleration two preferences were found. One group that
preferred a powerful and loud sound and another that preferred a refined full load sound
[13].
2.4 Interior sound in cars
The interior sound of a car consists of contribution from a numerous different sources such
as wind noise, road noise, driveline rumble, squeaks and rattles from within the driving
compartment. Some of these sounds work as feedback for the driver relaying information
about the vehicles current performance in terms of speed, acceleration or eventual
malfunction while other sounds can be a direct source of annoyance. If the overall noise is
rated to be good or bad does not merely depend on loudness but on spectral character as
well [12, 14, 15]. This is also the case for the annoyance of loud noise in steel plants [16]
and aircraft noise [17]. Sottek et al. mentioned the psychoacoustic metrics tonality,
sharpness, articulation index and roughness as playing a part in the judgment of interior
noise annoyance in cars [12]. Ellermeier et al. also suggests a coupling between the psycho
acoustic metrics loudness, sharpness and roughness to annoyance [18]. Another aspect of
annoyance is what is causing it. Ellermeier et al. showed that there are differences in
annoyance ratings between an identifiable or neutralized sound caused by the effect of the
"meaning" of the sound [18].
Sung-Hwan Shin et al. stated that the booming sensation in a car, related to engine order, is
a big contributor to interior noise annoyance. Booming typically appears in the frequency
range 30-120 Hz where the second and fourth engine orders are deemed to have a strong
effect. Auweraer et. al. mentioned correlations between loudness and the sensation of
booming [19]. This I also seen in Sung-Hwan Shin et al.'s study on cars with four cylinder
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internal combustion engines (ICE) during acceleration. In Sung-Hwan Shin et al.’s study
they also found a correlation between pitch and the booming sensation [20]. Another
addition to annoyance can be howling. Howling differs from a booming metric in frequency
whereas howling is related to higher frequencies. Hans and Weber describe how the
perception of howling, in the interior of a coasting car (ICE), can be done via the use of
alterations to the signal/noise-ratio or tone length. In this case the howling sensation is
produced by a tone at 517 Hz and howling decreases when the tone is lowered in level or
the tone length is shortened [6]. Gear whine is another example of a cause to annoyance
inside a car’s passenger compartment. It originates from the interaction of cogs and
produces a whining noise coupled to the rpm of the engine. This noise typically appears in
high frequencies and can be difficult to subjectively assess. The difficulty stems from the fact
that it is very hard to compare tones of different amplitude if they are not played close to
each other in time. Adding to the difficulty is the presence of additional tones originating
from other sources which are not to be judged as a part of the gear whine. The judgment is
also affected by the duration of the tones [7]. The effect of different durations of a tone has
also been investigated by Sköld et al. Their study also includes alterations made to the
amplitude of the tone and the effect of amplitude modulation. In their principal component
analysis the following dimensions were found: quality, audibility, tonal content, safety and
modulation. All of these dimensions are dependent on the properties of the tone and
confirm that tonal content in the interior noise of a car affect how the noise is rated. An
increasing amplitude of the tone leads to lower ratings of quality and safety. For modulation
frequencies above 32 Hz the audibility of the tone decreased whilst quality and safety
displayed a minor increase [10].
Jennings et al. gathered the results from a large number of studies assessing vehicle interior
or exterior noise for a number of driving conditions and vehicle types. They suggest that the
factors associated with the subjective evaluation of the sounds can be combined into two
underlying perceptual dimensions; power and comfort. These two dimensions are then
related to attributes describing the sounds in each study. The conclusion drawn is that
roughness or rumble affect comfort in a negative way but may also contribute to a sensation
of power. Sounds with fluctuating SPL can be a source of discomfort. A change in the low
frequency domain at an initial stage can add to a more powerful judgment of the sound. A
low level of sharpness can lead to higher ratings of the attribute powerful. Loudness can
either decrease comfort or enhance the feeling of power [21].
If alterations are made to the noise inside the passenger compartment of a car it changes
the feedback a driver is expecting to hear when driving. Wang and Wang have conducted a
study in which the driving speed of a car was estimated. The interior sound of a car was
modified in two ways; Frequencies less than 600 Hz or over 600 Hz were lowered 20 dB.
The abatement of low frequencies helps with speed estimation and may in extension be of
importance to road safety [22].
2.5 The electric vehicle
The interior noise level in an EV is lower than in a normal ICE car and has a different
frequency content. An ICE is more prone to have more low and mid frequency content
(below 1000 Hz) and an EV can have prominent high frequency tone components (>0.5
kHz). The high frequency tones are usually most prominent for acceleration but also for
deceleration when the motor works as a generator. The high frequency content can be a
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source of unpleasantness as shown in two studies, one by Govindswamy and Eisele [15] and
one by Lennström et al. [23]. They found that a reduction of the SPL of tones above 1 kHz
had a positive effect on subjective evaluations of pleasantness and a negative effect on
annoyance. Govindswamy and Eisele also state that the reduction of frequencies between
0.5 and 4 kHz for full load cases has a negative effect on how sporty and dynamic the car is
perceived and that the sporty feeling can be improved by the addition of low frequency
orders. In their study they also found that people disliked when sounds from an ICE was
added to an EV.
The lack of combustion engine noise in an EV reveals new unfamiliar or former unheard
sounds to the driver. These may previously have been masked by the combustion engine
noise. Otto et al. shows that the wind noise in an EV is subjectively rated as louder than the
wind noise in an ICE for constant speed conditions, even though the wind noise itself has
the same loudness value. The reason is that the wind noise in the ICE car is masked by the
engine noise and thus perceived as quieter. This holds true for high engine/wind noise
values corresponding to speeds up to 100 km/h. At speeds above 100 km/h the wind noise
factor increases and the masking effect is no longer dominating resulting in a more equal
judgment of EV and ICE wind noise. Otto et al. also mentions gear whine as a source of
annoyance in acceleration and deceleration. Gear whine is judged less annoying for
deceleration than acceleration because of a more even SPL during the deceleration
sequence [24].
2.6 Electric motor acoustics
An electric motor converts electric energy to kinetic energy. During this process vibrations
and acoustic noise is produced as a side effect. There are three types of dominant areas to
the origin of noise and vibration. These are mechanical, aerodynamic and electromagnetic.
From the mechanical part the sources can be brushes or bearings, unbalance or surface
imperfections of the rotor. Aerodynamic or flow induced noise can originate from the fan.
Electromagnetic noise sources include the PWM switching frequency and radial forces
acting on the stator. The radial forces acting on the stator is the most dominant noise and
vibration source whilst the two other noise sources, mechanical and aerodynamic, should
be negligible in the electric motor used for a car. If the motor’s excitation frequency
coincides with the stator’s natural frequency the stator becomes a powerful radiator of
acoustic noise [25]. Motor orders are coupled to the number of slots and poles in the motor.
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2.7 Statistics
Statistical methods
ANOVA [26]
Analysis of variance (ANOVA) can be used to compare different mean values originating
from one or more groups. Assumptions for the analysis are equal standard deviations and
normally distributed answers. Two types of ANOVA can be done depending on the design of
the experiment, between subjects or within subjects. A between subject design compares
two or more groups that are exposed to one test condition in each group. The within subject
design exposes every subject to each test condition. Advantages of the within subject design
is that it handles how people use for example scales differently and therefore finds
significant differences between test conditions better than between subject designs.
If there is only one dependent and one independent variable the test is called one-way
ANOVA and in the case of multiple dependent variables the test is called multifactor ANOVA.
In a one-way ANOVA it is possible to tell if there are significant differences between the
means of groups by looking at the F-ratio and p-value. The F-ratio compares the variance
between subjects to the variance between groups. If the variances are similar or exactly the
same the F-ratio is close to or exactly one. If there are differences between the variances the
F-ratio will be bigger than one. Related to the F-ratio is the p-value obtained in the ANOVA.
If the p-value ≤ 0.05 there is a difference between groups at a 95% confidence level. When
displaying the result of an ANOVA the mean and confidence intervals are plotted. If the
confidence levels overlap there is no significant difference between the groups at the given
confidence level. The confidence intervals are usually formed for each pair using a
technique call least significant difference (LSD). Another way to form the confidence limits
is by using Tukey's Honest Significant Difference (HSD). Tukey's HSD is good to use when
there are more than two groups because it corrects for the increased probability of finding
differences when multiple pairwise comparisons are made, and thus widening the intervals.
Regression analysis [27]
Regression analysis is an attempt to describe one or more variables effect on each other. For
example a linear regression analysis can be done comparing an increase in number of
apples (independent variable) to their total weight (dependent variable). Care needs to be
taken not to use regression analyses to find connections between arbitrary events but
rather use it to confirm carefully controlled experiments. Correlation between arbitrary
events can exist and is called nonsense correlation and give no specific information about
why a connection exists. In order to perform regression analysis some assumptions are
made. The data should be normally distributed around the regression line and the standard
deviation should be constant. By plotting the studentized residuals against the independent
variable it is possible to check an even standard deviation. If the plot has a funnel shape it
may be the result of a non-constant standard deviation. By doing a normal probability plot
of the residuals the assumption of normal distribution can be checked. There are some
important parameters which reveal information about a conducted regression analysis. The
10
correlation coefficient C describes the linear relation between the variables. A number one
corresponds to a perfect linear relationship. The R2 value is a predictor of how well the
model explains the variability of the dependant variable. There is also an adjusted version
of the R2(adj) which can be used when comparing regression curves with a different
number of independent variables. The R2 value should be high. A P-value is also calculated
which can be used to determine if any relationship between the variables exist (at a certain
confidence level). If a P-value is below 0.05 you can say that "at a confidence level of 95% a
relationship between the variables exist". Regression analysis can be used to compare two
or more sets of data. The results are lines for each set of data. The comparisons possible
here are to compare if there is any statistical difference between the intercepts or the slopes
of the lines.
11
3. Method
The following five steps were followed throughout the project in order to achieve the goals
and answer the research questions.





Recording
Suppress tones using filters
Create sinus-tones with Pure Data and Artemis
Add tones to filtered material
Evaluate with listening test
3.1 Recording
The recordings were made using an artificial head, HMS IV from Head Acoustics, situated in
the passenger seat of a Volvo C30 electric (see Figure 3).
A Data Rec 4 front-end and recording software from Head Acoustics Gmbh was also used.
The sampling frequency of the recorded material was set to 44.1 kHz and the artificial head
used ID filtering. The CAN-signals rpm and vehicle speed were extracted from the car and
recorded simultaneously with the artificial head. The sound files were recorded at Volvo’s
test track on two road surfaces; smooth pavement and rough pavement. Two driving
conditions were recorded; acceleration and constant speed. Accelerations were recorded
with maximum acceleration from 0-100 km/h. Constant speed recordings were made in 50
km/h and 80 km/h. The acceleration case was chosen because the tones are clearly audible
during this driving condition when the electric motor is working at maximum load/power
outtake. But a drawback of the acceleration case is that the tones are not stationary and the
frequency dependence of annoyance might be hard to quantify. Furthermore there are
psychoacoustic aspects of a ramping sound to consider. The tone might be more easily
detected because the mind has cues on what is happening in the sound file. Therefore a
good complement to the acceleration case is the constant speed conditions. In this case the
tones are not that easy to detect when driving the car or listening to the recorded material
but the frequency dependence of annoyance is easier investigated than in the acceleration
case.
12
Figure 3: Position of the artificial head.
3.2 Suppressing tones with filters
The recorded material was screened aurally with a Head Acoustics playback equalizer and
visually in Artemis with FFT or PR alternatively FFT versus rpm or PR versus rpm to find
tones. By the use of filters it was then possible to suppress specific tones generated by the
electric motor. In the following work the two channels recorded by the artificial head are
averaged when visualizing the results in FFT or PR calculations. This is a simplification that
may lead to small errors because the two channels are not identical. But it was chosen to be
performed this way due to work efficiency. Creating filters for one combined channel takes
half the time compared to creating two filters, one for each channel.
3.2 -Constant speed
In a constant speed condition the tones generated from the electric motor can be calculated
if the rpm of the motor is known. If for example the engine has a rpm of 7030
revolutions/minute a 6 pole motor will have a strong fundamental order of 703 Hz. The
frequency can be calculated using equation (1).
frequency  (rpm / 60) * n where n=the order number.
(1)
In Figure 4 a FFT and PR calculation for a constant driving speed condition at 7030 rpm is
shown. In a and b the original is used and in c and d the filtered file is used. The peak at
≈700 Hz is shown in a and in b. For the filtered file the peak is reduced roughly 6 dB and it
has no value for prominence ratio. This procedure was performed in all constant speed
cases were there were tones with any values of prominence ratio above zero.
13
a),
b),
c),
d).
Figure 4: a) FFT and b) PR calculations in the original file, c) FFT and d) PR calculations in the
filtered file. Constant speed driving condition with a rpm of 7030.
14
3.2 -Acceleration
It is possible to control the filter in a way that it tracks a specific order and therefore
changes frequency simultaneously with a changing rpm. The goal of the filtering process is
to lower the tone to the background noise level. If the tone is lowered more than that it may
result in a big change in sound and it may also lead to a big problem when calculating the
prominence ratio. If two orders are heavily filtered and they are close to each other in
frequency the results may be that the high amplitude value between the two tones is
interpreted as a tone with a high prominence ratio. This is shown in Figure 5 where order
14 has a high prominence ratio due to a heavy filtering of orders number 12 and 18. The
lower amplitude of order number 12 and 18 is shown in Figure 6.
Order 14
Figure 5: Left, PR versus rpm for an original recording. Right, PR versus rpm for
a too heavy filtered recording.
Figure 6: Left, FFT versus rpm for an original recording. FFT versus rpm for a too heavy
filtered recording.
15
A solution to the problem of unintentionally creating tones is to use filters with nonconstant amplitude. This means that the amplitude of the filter is varied over time or rpm.
In this way the peaks of certain orders can be suppressed much more than the rest of the
order resulting in a much more even order level. Figure 7 shows an example of how the
amplitude of the filter may be altered during an rpm sweep. The level of the filter is always
below 0 dB which means that it is constantly suppressing the order level. It is also visible
that there is less suppression of the tone from 8 kHz and upwards than for example
between 4,5 kHz and 7 kHz.
Figure 7: Amplitude of a variable filter.
16
In Figure 8 an unfiltered WOT-acceleration is displayed in the form of a FFT- and a PRversus rpm and two resonance peaks are marked. WOT (Wide Open Throttle) for a vehicle
with an ICE means that the engine is taking in a maximum amount of air and fuel. This takes
place when the gas-pedal is fully depressed and is used in the thesis to describe that the
electric vehicle is running at maximum acceleration.
Figure 8: To the left FFT vs. Rpm and to the right PR vs. rpm, unfiltered.
Figure 9 displays a FFT- and a PR- versus rpm when order tracking filters with variable
amplitude has been used to suppress the order levels and especially the two peaks. The
previously marked tones are now gone but there are still tones visible in the PR vs. rpm
figure. These tones are however not order-related. The clearest tone lies constantly fixed at
about 1000 Hz throughout the acceleration and may be a result of the tire cavity resonance.
From the FFT vs. rpm there are no longer any tones visible but only small valleys where the
tones have been.
Figure 9: To the left FFT versus rpm and to the right PR versus rpm. Filtered width variable
filters.
17
In Figure 10 the level in dB is displayed for each order. The prominent orders visible in the
green curve stem from the unfiltered WOT-acceleration. The red curve is the same but
filtered version of the file. The orders have been lowered to approximately the background
noise level.
6
12
18
28
36
Figure 10: Order against dB. Green is unfiltered and Red is filtered.
18
3.3 Create sinusoidal tones with Pure Data and Artemis
3.3 -Constant speed
Creating sinusoidal tones for constant speeds were done using the signal generator in
Artemis. In this module it is possible to specify the length (s), amplitude (dB) and frequency
(Hz) of the tone. One tone was created to have a PR of 0 and then several more were created
with a stepwise increase in amplitude of 2 dB. Between six and ten tones per frequency
were created. The frequencies are representations of three dominant motor orders, 6, 36
and 72. These orders were chosen because they give a wide frequency area to analyze.
3.3 -Acceleration
In order to create a tone for an accelerating driving condition the tones have to follow the
rpm of the engine which increases over time. This was done by the use of the software
synthesizer Pure Data. Pure data allows the user to create and control specific waveforms
such as a sinus by visually connecting predefined objects with strings. In Figure 11 the
master program is displayed and the four parts of the program are circled with red circles.
The first part of the program controls the amplitude of the tone.
1.
2.
4.
3.
Figure 11: The master program used for creating tones in Pure Data.
19
The second part shown in Figure 12 is a so called sub patch which controls the ramping of
the tone. In the table to the right rpm values sampled at 4 Hz are saved from the CAN signal
of the car. The values are then read in succession 0.25 seconds apart and sent to the master
program via an outlet at the bottom left.
Figure 12: The second part of the Pure Data program. The sub patch controlling the frequency
ramping.
The third part of the program, displayed in Figure 13, receives the rpm values previously
read and forwards them to an algorithm which converts an rpm value into a specific
frequency for a given order. The calculation is shown in equation (1).
The frequency is then sent to a line object. The line object acts as a counter, counting first
from zero then to a given value, in this case the first frequency value. When the line objects
then receives another value it continues counting from the latest value it was given to the
new value it has received. In this way a continuous ramping of the frequency is
approximated. The values from the line object are sent to a sinus signal generator named
~osc. After that object the signal becomes an audio signal which is sent to the master
program.
20
Figure 13: The third part of the Pure Data program. The sub patch used for creating the audio
signals.
In the fourth part in the master program the signal’s length is defined and then the file is
saved as a wave file.
21
3.4 Add tones to filtered material
In this step the filtered files and the tones are merged using Artemis' merge editor. With the
use of the merge editor a sound file is created for each tone and amplitude, leading to a total
of 121 sounds with a PR for each tone ranging from ≈0 to ≈7 dB. In Figure 14 the effect of
adding a tone to a sound file is visualized in a FFT versus rpm for three levels of the tone. In
the rightmost part of Figure 14 the tone can clearly be seen as a yellow arc ranging from
about 100-1000 Hz. In Figure 15 a FFT is shown for tones added to a constant speed driving
case. The tone is added at 446 Hz. From left to right the tone level is increased between the
FFT calculations and the tone is clearly visible as a peak in the rightmost part of Figure 15.
Figure 14: FFT versus rpm with an added tone (order 6) created in Pure Data.
Figure 15: FFT of a constant speed driving condition with an added tone at 446 Hz.
22
3.4.1 Calculating prominence ratio
The prominence ratio was calculated in Artemis using their existing tools. The settings in
the FFT used for calculations of the constant speed prominence ratios were 16384 numbers
of lines and 50% overlap. This setting leads to a Δf (frequency width) of ≈2,7 Hz. With this
setting the peak value of prominence was stable. Increasing the resolution further did not
create more accurate values, only longer calculation times. The settings for acceleration
cases were 4096 frequency lines leading to a Δf of ≈11 Hz. This setting was used because it
made the orders visible in a PR versus rpm calculation. The calculations were performed for
every fifth rpm value of the acceleration driving condition. For both constant speed and
acceleration the setting show tones only and compensate for threshold of hearing were used.
When enabling show tones only the file is screened for tones, meaning that a tone is detected
if it is 6 dB above a 24th oct. smoothed filter and the PR is only calculated for tones fulfilling
that criteria. Compensate for threshold of hearing means that if the noise in the background
of the tone is below the threshold of hearing it is raised in level to the threshold value. If the
latter option isn’t used tones that aren’t audible may still have a high PR value.
3.4.1 -Constant speed
For the constant speed condition calculations of PR for each file is done via the Artemis
function Prominence ratio. In Figure 16 the FFT and PR is shown for three levels of the same
tone.
Figure 16: Top three, FFT. Bottom three, PR. Three levels of a tone at 446 Hz.
23
3.4.1 -Acceleration
Since an acceleration driving condition and its PR versus rpm contain many values of PR a
specific method has been developed in order to choose one PR value to represent the sound
file. The PR chosen is the maximum value of PR from a specific order which does not lie in a
steady resonance band. The maximum value is chosen because if the tone is at the threshold
of audibility it stands to reason that it is the highest PR value that remains audible. A just
audible order 36 in an acceleration file is shown in an FFT versus rpm plot in Figure 17. The
tone is only heard in the last part and this is where it is most visible as well.
Order 36
Figure 17: FFT versus rpm for a just audible tone.
The PR versus rpm shows that there are higher values of PR in order 36 than the ones in the
end but these lie in the resonance areas marked with the orange circle in Figure 18. The
resonances in these areas are not dependent on the vehicle speed and are constant
throughout the acceleration. These resonances can be caused by for example tyre threads or
the excitation of the tyre cavity and can have a somewhat tonal appearance. Because there
already are values for prominence in these areas they cannot be chosen as the maximum
value because they are not a result of adding the tone. Furthermore the tone is not heard in
these areas because it is masked by the background noise. Therefore the PR should be
chosen from within the white dotted circle. In this area it is the tone that gives a reading to
the PR. When the PR is chosen it is said that a certain PR value belongs to an order and the
frequency range the order covers.
24
Order 36
Resonance
Figure 18: PR versus rpm for a just audible tone.
3.5 The sound files
In total there were 121 sound files used in the listening test for testing the audibility
threshold and ratings of annoyance and well sound. In all sound files the tones that were
added were either a representation of order 6, 36 or 72. In the different driving conditions
this means that the orders differ in frequency. The driving cases, orders and frequencies are
compiled in Table 1. Each line in the table represents a test condition and each of the test
conditions consisted of about 6 or 7 sound files with different values of PR.
Table 1: Information about the sound files and tones used in the listening test.
DRIVING CONDITION
ROAD SURFACE
ORDER #
FREQUENCY (HZ)
50 km/h
50 km/h
50 km/h
50 km/h
50 km/h
50 km/h
80 km/h
80 km/h
80 km/h
80 km/h
80 km/h
80 km/h
Acceleration
Acceleration
Acceleration
Acceleration
Acceleration
Acceleration
Smooth
Smooth
Smooth
Rough
Rough
Rough
Smooth
Smooth
Smooth
Rough
Rough
Rough
Smooth
Smooth
Smooth
Rough
Rough
Rough
6
36
72
6
36
72
6
36
72
6
36
72
6
36
72
6
36
72
446
2676
5352
446
2676
5352
703
4220
8440
703
4220
8440
114-842
685-5050
1370-10100
58-842
345-5050
690-10100
25
3.6 Evaluate with a listening test
3.6.1 Listening test procedures
In the field of noise and vibration a common way to evaluate sound quality and to find how
objective measurements correspond to subjective ratings is by performing a listening test.
The basic principle is that a number of subjects listen to a number of sounds and rate or
describe what they hear. It is then possible for the conductor of the experiment to find out
preferences which can be said to be valid for a specific group and be representative for a
larger population. A listening test can be conducted in different environments, for example
an anechoic chamber or a car. The choice of environment depends on what is researched. In
Lennström et al. there were no significant differences in the results when the sound of
electric cars were judged in three different environments, inside the real car, inside a
stationary car fitted with speakers and headphones or inside a room designed for listening
tests at Volvo [23].
If recorded sounds are used in the listening test it is important that they are good
representatives of the real sounds in terms of frequency range and loudness. A common
technique to obtain good quality recordings is via binaural head recordings. A binaural head
recording is done with a artificial head fitted with microphones inside the ear canal. An
artificial head is shown in Figure 19. This technique helps to reproduce two vital properties
of how humans interpret sound, inter aural level difference (ILD) and inter aural time
difference (ITD). The ILD is created by the shadowing effect of the head resulting in
differences in SPLs between the ears or microphones. ITD is created when an incoming
sound wave reaches one microphone before the other resulting in a time difference
between the ears or microphones [2]. When recording with an artificial head it is possible to
add filters which remove the effect of the pinna and the ear canal resonance of the artificial
head. A recommended filter for evaluating product sound quality is the independent of
direction (ID) filter from Head Acoustics [28]. Playback of the recorded material is usually
done via headphones and a playback equalizer enabling a binaural playback and an equal
SPL in playback and recording position.
Figure 19: An artificial head for binaural recordings.
26
Choosing subjects to be used in a listening test can be done on different basis. Specialized or
trained subjects may be used if test demands judging of a certain aspect of the sound.
Randomly picked people or potential customers from a population are recommended for
listening tests in the automotive industry. Subjects should be tested or asked if they have
any known hearing loss. A recommendation of the number of participants depends on the
type of subjects but when regular people are being used 25-50 persons is recommended
[28]. A listening test should not be too long, more than one hour, due to fatigue problems of
the participants.
The subjects may be asked to perform different tasks in different listening tests. Three
common tasks are detection, ranking order and response rating on a scale. The detection
task asks the subject to detect a signal or property of a sound. Ranking order asks the
subject to rank a number of sounds in a specific order. Response rating on a scale can be
done on either a bipolar or unibipolar scale. An example of a unibipolar scale might be to
rate loudness from zero to ten where zero is not loud at all and ten is extremely loud.
3.6.2 The subjects
In order to evaluate the material and be able to answer the research questions a listening
test was conducted and performed by 30 individuals. Three subjects were deleted from the
results because they had some form of hearing impairment. Of the remaining 27 subjects,
15 were male and 12 female and they ranged in age between 53 and 21. The mean age was
32,6 and the median was 29. 20 of the subjects worked at Volvo and 7 did not. None of the
subjects were experienced in the field of noise and vibration.
3.6.3 The listening test
A written introduction, shown in appendix 1, was given to each test participant before the
test started. The test was divided into four parts. The first consisted of an introduction to
familiarize the subjects with the type of coming sounds. Six different sounds were chosen
from the material which varied in tonal prominence and pitch. After the introduction the
subjects were given time to ask questions if anything about the test was unclear.
The second part consisted of listening to and rating randomly chosen sound files from all
the material. The participants answered the questions displayed in Table 2 for each of the
121 sounds. The subjects answered with a cross to mark if the tone was heard, if they were
unsure or if they didn't hear. The sounds were also rated in two categories, annoying and
well sounding, on a scale from 0-10. A value of 0 corresponded to not at all annoying or well
sounding and 10 corresponded to maximum annoying or well sounding. It was explained to
the subjects that when rating annoying or well sounding it was important that they
envisioned themselves sitting in a car and judging the sound on that premises. The subjects
were also told to write a judgment on the sound whether they heard a tone or not. In that
way the effect the tones have can be found. The word annoying was chosen as an attribute
to rate because its meaning is easily understood. An annoying sound has a clear negative
meaning and is unwanted in the interior of a car. The attribute well sounding was explained
to not be the exact opposite of annoying. Instead the ratings of well sounding could depend
on whether the subject thought that the sound carried some information about the vehicle
performance or if the tone in the sound was a positive contribution.
27
Table 2: The questions in part two of the listening test.
Did you hear a
Rate the sounds with a number from 0-10
tone/beep?
How annoying was
How well sounding was
Sound #
Yes
Unsure
No
the sound?
the sound?
1
The third part of the test consisted of judging three sounds. One original recording of
acceleration, one designed sound and one filtered version. The designed file was created by
filtering the original acceleration recording in a way that there were prominent tones in the
beginning and no prominent tones at the end. There were no added tones to this file. The
tones heard in the beginning were existing tones being amplified by the same filters used to
suppress the tones later on in the file. This way the aural feedback of the motor was kept
whilst reducing high frequency tones at the end. The filtered file was filtered to contain as
little tonal content as possible. In the fourth part of the test the subjects were given
questions of a more subjective nature. The questions are displayed in Table 3. The subjects
were also asked to write any comments on how they perceived the sound files in the end.
Table 3: Questions in the third part of the listening test.
Do you believe in electric drives as the future for vehicles?
Mark your answer:
Yes Maybe No
How do you want a car/electric car to sound when you drive it?
Mark your answer(s):
Sporty
sound
Very quiet
As a petrol/diesel car
Prominent engine
Low rumbling
As a spaceship
Not as a petrol/diesel car
Doesn't matter
28
The results from the listening test were analyzed in Microsoft Excel and the statistical
software Statgraphics to assess the audibility limit, annoyance, well sounding, subjective
ratings and Tone in 1/3-octave band.
-Audibility limit
In excel the audibility limit was examined by plotting the percentage of yes answers against
PR for each driving condition, road surface and frequency.
-Annoyance
In order to predict how annoyance is affected by increasing PR for different frequencies
regression analysis was used. The statistical method ANOVA was also used to compare the
different frequency regions for each driving case and road surface.
-Well sounding
When investigating the ratings of well sounding the goal is to root out if there are any
differences between the frequencies. Therefore the analysis of well sounding was made by
comparison of regression lines.
-Subjective ratings
In the third and fourth part of the listening test the subjects made choices on which sound
they preferred or how they wanted a car/electric car to sound. The results are simply the
number of subjects who chose a sound of a preferred sound character.
-Tone in 1/3-octave band
The threshold of audibility for PR and for a Tone in 1/3-octave band analysis was compared
in one driving case to see how they relate to each other.
29
4. Results and Discussion
The results from the listening test are divided into these parts:




The threshold of audibility
Analyzing annoyance and well sounding
Comparison with the tone in 1/3- octave band method
Subjective ratings
In first three sections the results are first displayed for the constant speed driving case and
then the acceleration.
4.1 The threshold of audibility
4.1 -Constant speed
The data from the part of the listening test where the subjects were asked to answer yes,
unsure or no on the question if they heard any tone or not is compiled in Figure 20. This
picture is an example of how the data looks like for the driving case 50 km/h, smooth
pavement and motor order 6. PR is rounded to an integer in order to be able to plot the
results. Beside yes, unsure and no there are also some people who didn't answer this
particular question displayed as no answer.
100
yes
Answers (%)
75
unsure
50
no
no answer
25
0
0
1
2
3
4
5
PR-level dB (rounded)
6
7
Figure 20: Compiled data from the analysis of the threshold of audibility (50 km/h, smooth
pavement and a tone at order 6 (446 Hz)).
30
Another way of visualizing the results is to only display the percentage of yes-answers. In
Figure 21 the yes-answers are displayed for order 6, 36 and 72 for driving in 50 km/h or 80
km/h on smooth or rough pavement.
100
% Yes
446
Hz
2676
Hz
5352
Hz
50
446
Hz
2676
Hz
5352
Hz
% Yes
100
50
0
0
0 1 2 3 4 5 6 7
0 1 2 3 4 5 6 7
PR-level rounded (dB)
a)
100
% Yes
703
Hz
4220
Hz
8440
Hz
50
% Yes
100
50
PR-level rounded (dB)
b)
703
Hz
4220
Hz
8440
Hz
0
0
0 1 2 3 4 5 6 7
PR-level rounded (dB)
PR-level rounded (dB)
c)
d)
Figure 21: Yes-answers (%) for a) 50 km/h smooth, b) 50 km/h rough, c) 80 km/h smooth and
d) 80 km/h rough pavement.
0 1 2 3 4 5 6 7
To summarize the results from the constant speed driving case (50 and 80 km/h, smooth
and rough pavement) the values of PR where more than 50% answered yes is gathered. The
result is shown in Figure 22.
Figure 22: Threshold where the tones become audible for more than 50% of people, constant
speed driving condition.
31
4.1 -Acceleration
In the acceleration driving case the percentage of yes-answers looks as in Figure 23.
100
100
Order 6
Order 6
Order
36
Order
72
Order
36
Order
72
50
% Yes
% Yes
50
0
0
0 1 2 3 4 5 6 7
PR-value rounded (dB)
PR-value rounded (dB)
a)
b)
Figure 23: Yes-answers (%) for acceleration files. To the left is smooth and to the right rough
pavement.
0 1 2 3 4 5 6 7
Figure 24 shows the PR where more than 50% heard the tone. The values are taken from
Figure 23 a) and b) and corresponds to the value of PR where each order have a percentage
of yes-answers above 50 percent.
3
PR-value (dB)
2,5
2
Order 6
1,5
Order 36 and
72
1
0,5
9000
8000
10000
Frequency (Hz)
7000
6000
5000
4000
3000
2000
1000
500
0
0
Figure 24: Threshold of audibility for the tones in the acceleration files.
32
4.2 Conclusion: Research question 1.
1. Find values of prominence ratio were tones become audible.
In both constant speed and acceleration, Figure 22 and Figure 24, the trend that lower
frequencies are harder to detect is evident. The values of PR where more than 50 percent
heard the tone can range from 1-4 dB for frequencies below 1 kHz. For higher frequencies
the tone is detected by more than 50 percent at PR-values of 0 dB. Using PR to describe the
level where tones are detected has not been widely used and the values found provide new
knowledge of the frequency dependence of tone detection. That the higher frequency tones
are easier to detect than lower frequency tones may be because they are not that common
in the frequency spectra of common ICE vehicles and thus become something that stands
out.
The trends shown in Figure 22 and Figure 24 correlates to the values in Figure 2 where
higher PR values are needed for low frequencies in order to be considered prominent.
These indicates that the human hearing is sensitive, both in detecting and passing judgment,
to higher frequencies (>1000 Hz).
33
4.3 Analysis of annoyance
A representation of how annoyance is rated is shown in Figure 25 for 50 km/h on smooth
pavement. The figure shows that the spread of each individual PR-value is quiet big. It also
shows a trend of increasing annoyance with increasing PR. An explanation to the large
width of the boxes is that each subject has used the rating scale in their own way.
a)
b)
c)
Figure 25: Box plots of annoyance ratings of 50 km/h smooth pavement. Figure a, b and c are
results from order 6, 36 and 72 respectively.
34
4.3.1 Annoyance as a function of PR
To investigate how an increase in PR affects annoyance a regression analysis was
performed for each driving case. Table 4 summarizes all regression analyses and displays
the correlation coefficient (C) and the R2 value. Each driving case also has a calculated
regression equation displayed in the rightmost column. The driving cases in the table are
ordered from highest C-value to lowest. A C-value of 0,53 indicates a moderately strong
linear relationship between annoyance and PR and a C-value of for example 0,21 is
considered to be relatively weak. A reason for not obtaining higher R2 and C-values is
perhaps because differences in how the subjects used the scale, shown in Figure 25. If all
subjects used the scale the same way the regression analysis may have been improved. This
might have been done by rephrasing the question in the listening test and telling the
subjects to give a annoyance rating of zero if they did not hear a tone. The regression
parameters may then show better linear correlation but the subjects are then more or less
only judging the tone and not the sound as a whole. Judging the sound as a whole was
thought to be most important and is the reason why the listening test questions was
formulated as it were. If the p-value is less than 0,05 there is a significant interaction
between annoyance and PR on a 95% significance level. This is the case for all but the last
driving case. Every regression equation has a positive sign which means that increasing the
level of PR results in an increased annoyance.
Table 4: Compiled data from the regression analysis.
Driving
condition
80
WOT
WOT
50
80
50
WOT
50
50
80
80
50
50
WOT
80
WOT
WOT
80
Road
surface
Smooth
Rough
Smooth
Rough
Rough
Smooth
Smooth
Smooth
Rough
Rough
Smooth
Rough
Smooth
Rough
Rough
Smooth
Rough
Smooth
Order #
p
C
36
72
72
36
36
36
36
72
72
72
72
6
6
6
6
6
36
6
0
0
0
0
0
0
0
0
0
0
0
0
0,0001
0,001
0
0,0042
0,016
0,25
0,53
0,51
0,49
0,45
0,44
0,43
0,41
0,4
0,38
0,38
0,35
0,34
0,29
0,28
0,27
0,25
0,21
0,071
Rsq
.28
. 26
.24
.21
.20
.18
.17
.16
.14
.14
.12
.11
.08
.08
.07
.06
.04
.05
Regression equation (Annoying=)
3,76107 + 0,764613*PR-value
2,53347 + 0,993139*PR-value
2,91205 + 0,941337*PR-value
4,01182 + 0,631541*PR-value
4,31781 + 0,594471*PR-value
3,70783 + 0,49961*PR-value
0,88882 + 1,30063*PR-value
3,93193 + 0,490683*PR-value
4,99478 + 0,493349*PR-value
4,26553 + 0,435101*PR-value
4,36672 + 0,366902*PR-value
3,07552 + 0,370765*PR-value
2,48528 + 0,388411*PR-value
3,09283 + 0,360533*PR-value
3,3253 + 0,369983*PR-value
3,21178 + 0,307039*PR-value
3,50908 + 0,289303*PR-value
3,60481 + 0,109827*PR-value
The regression analysis with the best linear correlation is order 36 for 80 km/h on smooth
pavement. This case is shown in Figure 26, its studentized residuals in Figure 27 and in
35
Table 5 the regression parameters are displayed. The regression line displays a clear
positive trend and the studentized residuals do not have a funnel shape which would
indicate a non-constant standard deviation. The regression parameters reveals a
moderately strong linear correlation (R-sq=0,53) and that there is a 95% certainty that
annoyance depends on the value of PR (p<0,05). From Figure 27 it is seen that for a PR of
zero the annoyance is not zero even though only 25% of all subjects heard the tone. This is
due to the fact that it was not only the tone that was being judged, it was the whole sound. If
a subject did not hear a tone they still needed to rate the sound.
Plot of Fitted Model
Annoying = 3,76107 + 0,764613*PR-value (dB)
10
Annoying
8
6
4
2
0
0
1
2
3
PR-value (dB)
4
5
6
Figure 26: Linear regression analysis of 80 km/h, smooth pavement and order 6.
Residual Plot
Annoying = 3,76107 + 0,764613*PR-value (dB)
Studentized residual
2,5
1,5
0,5
-0,5
-1,5
-2,5
0
1
2
3
PR-value (dB)
4
5
6
Figure 27: Studentized residuals the linear regression analysis of 80 km/h, smooth pavement
and order 6.
36
Table 5: Regression parameters from the analysis of 80 km/h, smooth pavement and order 6.
Explanatory parameters
Correlation Coefficient = 0,531373
R-squared = 28,2357 percent
R-squared (adjusted for d.f.) = 27,8457 percent
Coefficients
Least Squares
Standard T
Parameter
Estimate
Error
Statistic
P-Value
Intercept
3,76107 0,273436
13,7548
.000
Slope
0,764613 0,089864
8,50853
.000
Analysis of Variance
Source
Sum of Squares Df
Mean Square F-Ratio P-Value
Model
405,423
1
405,423
72,4
0
Residual
1030,43
184
5,60015
Total (Corr.)
1435,85
185
4.3.2 The frequency dependence of annoyance
To evaluate if annoyance depends on the frequency of the tone the regression lines for each
driving case, speed and road surface, were compared. When comparing regression lines the
slopes and interception points are examined. A p-value < 0,05 for slope or interception
indicates that there is a 95% percent certainty that there is a difference between the
compared regression lines.
4.3.2 -Constant speed
For every constant speed driving case the interception of the regression lines representing
each order are significantly different. The high frequency orders 36 and 72 are in most
cases higher than order 6. This means that both of them are considered more annoying than
the low frequency order 6. The slopes of the regression lines are in all cases considered
equal with no p-value < 0,05. In Figure 28, Figure 29, Figure 30 and Figure 31 the
regression lines for the constant speed conditions are shown. In Table 6,
37
Table 7, Table 8 and Table 9 the regression parameters for each constant speed condition
are shown.
Plot of Fitted Model
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure
1: Figure
1: Linear
Linearregression
regression
models
of order
3672,
and
50 km/h
Figure 28:
Figure 1:
models
of order
6, 366,and
5072,
km/h
smoothsmooth
pavement.AAtotal
total
547
values
were
pavement.
ofof
547
values
were
used.used.
Table 6: ANOVA table for intercepts and slopes, 50 km/h smooth pavement.
Source
Sum of Squares
Df Mean Square
F-Ratio
P-Value
PR-value (dB)
498,671
1
498,671
82,71
0
Intercepts
309,963
2
154,981
25,71
0
Slopes
5,47755
2
2,73877
0,45
0,6352
Model
814,112
5
Plot of Fitted Model
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 29: Linear regression models of order 6, 36 and 72, 50 km/h rough pavement.
A total of 551 values were used.
38
Table 7: ANOVA table for intercepts and slopes, 50 km/h rough pavement.
Source
PR-value (dB)
Intercepts
Slopes
Model
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
401,531
1
401,531
76,09
0
470,315
2
235,157
44,56
0
24,1388
2
12,0694
2,29
0,1025
895,985
5
Plot of Fitted Model
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 30: Linear regression models of order 6, 36 and 72, 80 km/h smooth pavement.
A total of 631 values were used.
Table 8: ANOVA table for intercepts and slopes, 80 km/h smooth pavement.
Source
PR-value (dB)
Intercepts
Slopes
Model
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
489,826
1
489,826
82,02
0
389,722
2
194,861
32,63
0
147,562
2
73,7812
12,36
0
1027,11
5
39
Plot of Fitted Model
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 31: Linear regression models of order 6, 36 and 72, 80 km/h rough pavement. A
total of 594 values were used.
Table 9: ANOVA table for intercepts and slopes, 80 km/h rough pavement.
Source
PR-value (dB)
Intercepts
Slopes
Model
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
610,705
1
610,705
95,29
0
266,975
2
133,487
20,83
0
22,1642
2
11,0821
1,73
0,1783
899,844
5
40
4.3.2 -Acceleration
The comparison of regression lines for the acceleration cases differs from the constant
speed condition. For both smooth and rough road surfaces the slopes are significantly
different as well as the intercepts. In these cases it is still the high frequency orders which
contribute to a higher annoyance than order 6. The linear regression analyses for smooth
and rough pavement can be viewed in Figure 32 and Figure 33 with their respective
Fitted Model
regression parameters in Table 10Plot
andofTable
11.
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 32: Linear regression models of order 6, 36 and 72, acceleration on smooth pavement.
Table 10: ANOVA table for intercepts and slopes, acceleration on smooth pavement.
Source
PR-value (dB)
Intercepts
Slopes
Model
Sum of Squares
Df Mean Square
F-Ratio
P-Value
288,607
1
288,607
48,57
0
124,884
2
62,4419
10,51
0
131,014
2
65,507
11,02
0
544,505
5
Plot of Fitted Model
10
Order #
6
36
72
Annoying
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 33: Linear regression models of order 6, 36 and 72, acceleration rough pavement.
41
Table 11: ANOVA table for intercepts and slopes, acceleration on rough pavement.
Source
PR-value (dB)
Intercepts
Slopes
Model
Sum of Squares
Df
326,337
1
241,422
2
109,636
2
677,395
5
Mean Square F-Ratio
P-Value
326,337
51,58
0
120,711
19,08
0
54,8181
8,66
0,0002
4.3.2 - ANOVA, Constant speed
To further examine the frequency dependence of annoyance an ANOVA was carried out. For
a fair comparison of annoyance a specific range of PR was chosen, 4-5 dB. In this range it is
known from investigation of the threshold of audibility that more than 50% of the subjects
heard the tone. Figure 34 and Figure 35 show the ratings of annoyance of the three tones
from 50 km/h and the three tones from 80 km/h for smooth and rough pavement
respectively. The ANOVA for smooth pavement consisted of data from 1178 observations
and the ANOVA for rough pavement consisted of data from 1145 observations. Both figures
reveal significant differences between the two lower frequencies to the four higher
frequencies for smooth and rough pavements respectively. If the boxes are not overlapping
there is a significant difference between them. The values are plotted with Tukey’s honestly
significant difference (HSD) intervals for comparison of multiple means. Table 12 and Table
13 show the values obtained in the ANOVA and they both indicate that significant
differences exist between the frequencies with p < 0,05.
Means and 95,0 Percent Tukey HSD Intervals
Means and 95,0 Percent Tukey HSD Intervals
6,6
7
6,1
Annoying
Annoying
6
5
5,6
5,1
4,6
4
4,1
3,6
3
446
703
2676 4220 5352 8440
Frequency (Hz)
Figure 34: Results of the ANOVA for smooth
pavement 50 km/h and 80 km/h. Means and
95% Tukey HSD intervals.
446
703
2676 4220 5352 8440
Frequency (Hz)
Figure 35: Results of the ANOVA for rough
pavement 50 km/h and 80 km/h. Means and
95% Tukey HSD intervals.
42
Table 12: ANOVA table for smooth pavement.
Source
Sum of Squares
Df
Between groups 784,733
5
Within groups
8104,79
1172
Total (Corr.)
8889,52
1177
Mean Square
156,947
6,91535
F-Ratio
22,70
P-Value
,0000
Table 13: ANOVA table for rough pavement.
Source
Sum of Squares
Df
Between groups 679,957
5
Within groups
7760,06
1139
Total (Corr.)
8440,02
1144
Mean Square
135,991
6,81305
F-Ratio
19,96
P-Value
,0000
4.4 Conclusion: Research question 2
2. Find out if tones from the electric motor are perceived as annoying and if so, are all
levels and frequencies equally annoying.
It can be seen from chapter 4.4.1 that for many cases annoyance can be described as having
a linear relationship with PR and that annoyance increase with increased PR. From chapter
4.4.2 it can be seen that not all frequencies are judged as equally annoying. Frequencies
higher than 703 Hz are being judged more annoying than frequencies below 703 Hz. That
annoyance increase with increasing tonalness is also found in previous studies [5, 8, 9, 10].
The level and frequency dependence of annoyance for the tones is important when dealing
with sound quality.
43
4.5 Analysis of well sounding
4.5 -Constant speed
Figure 36 displays the comparison of regression lines for well soundingd for all constant
speed cases (smooth and rough road surface and 50 and 80 km/h). A total of 2335 values
from the listening test provide the data for the regression lines. Each regression line
corresponds to a regression analysis done for how well sound depends on increasing PR.
From the figure it is possible to see that the two lowest frequencies are rated higher on well
sounding than the frequencies above 703 Hz. Table 14 displays the regression parameters
from this comparison. The p-value for intercepts is lower than 0,05 which indicates a
Plot of Fitted
Model
significant difference at 95% confidence
level.
10
Frequency (Hz)
446
703
2676
4220
5352
8440
Well sounding
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 36: Comparison of the regression lines for constant speed conditions, 50 and 80 km/h,
smooth and rough pavement.
Table 14: Regression parameters for the comparison of regression lines of all frequencies,
smooth and rough road surface and 50 and 80 km/h.
Source
Sum of Squares Df Mean Square F-Ratio P-Value
PR-value (dB)
Intercepts
Slopes
Model
458,613
481,12
53,5987
993,332
1 458,613
5 96,224
5 10,7197
11
77,12
16,18
1,80
0,0000
0,0000
0,1090
4.5 -Acceleration
The regression lines in Figure 37 are all from the acceleration driving cases. A total of 842
values were used in the analysis. The lines are a representation of which frequencies they
span. The thicker the line the higher the frequencies in the frequency span. Three of the
lines with the biggest width and therefore highest frequencies have a steeper decline than
the three others. This suggests that with increasing PR the tone is rated as less and less well
sounding. The regression parameters in Table 15 show that significant differences exist
between both intercepts and slopes.
44
Plot of Fitted Model
10
Frequency (Hz)
114-842
1370-10100
345-5050
58-842
685-5050
690-10100
Well sounding
8
6
4
2
0
0
2
4
PR-value (dB)
6
8
Figure 37: Comparison of the regression lines for accelerating driving conditions.
Table 15: Regression parameters for the comparison of regression lines of all frequencies,
accelerating driving conditions.
Source
Sum of Squares Df Mean Square F-Ratio P-Value
PR-value (dB)
Intercepts
Slopes
Model
246,062
222,046
150,213
618,321
1 246,062
5 44,4092
5 30,0426
11
33,88
6,11
4,14
0,0000
0,0000
0,0010
45
4.6 Tone in 1/3-octave band
This method compares the level of a tone and the level of the 1/3-octave band in which the
tone reside. The tone should be roughly six dB lower in level than the 1/3-octave band to be
considered inaudible. The FFT parameters used in the calculations are 65536 frequency
lines 50% overlap and flat top windowing for the tone and Hanning window for the 1/3octave band. A FFT and a 1/3-octave band analysis are shown in Figure 38 for 50 km/h on a
smooth road surface. The frequency of the tone added to the file is 2676 Hz and is seen as a
peak in the red spectrum. In this case the level of the tone is about 4 dB lower than the one
third octave band level.
Figure 38: Tone in one third octave band analysis of a tone at 2676 Hz.
In Figure 39 the percentage of yes-answers are plotted against the difference between the
1/3- octave band level and the level of the tone for 80 km/h on rough pavement. Order 36
at frequency 4220 Hz is the first tone to be detected by more than 50% of the subjects at -8
dB. In second place order 6 at 703 Hz is detected at -5 dB. Lastly order 72 at 8440 Hz is
detected at a level as high as -1 dB. That the tone at 8440 Hz is detected so late is also seen
in Figure 39 were it takes a PR of 3 in order to get more than 50% of the subjects hearing
the tone. This might be because it could potentially be hard for the oldest people in the test
to hear the tone. Just because they heard the test tone at 10 kHz in the screening for the
listening test does not mean that they hear high frequencies just as good as younger people.
There were more subjects who did not hear the tone at 8440 Hz than the tone at 5352 Hz.
46
100
% Yes
703 Hz
50
4220
Hz
8440
Hz
0
-14-13-12-11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
Lp_1/3oct.-Lp_tone (dB)
Figure 39: Yes-answers (% of subjects) for the tone in 1/3 octave band analysis of 80 km/h
rough pavement.
4.7 Third and fourth part of the listening test - Subjective ratings
In the third part of the listening test the subjects listened to three sounds. The first was an
original binaural recording of acceleration then a filtered version of that same recording
which had designed tones. The last sound file was the original recording but filtered to
contain a minimum of tones. In total 24 people voted for their favorite sound. The first
sound got six votes, the second five and the third thirteen votes. The results show that the
sound with the least tonal content was preferred. This sound may have been perceived as
the quietest of the sounds since it was filtered to decrease the level of all clearly audible
tones. Why the sound with designed tones proved to be least favored might have its
explanation in the construction of the listening test. First the subjects listened to sounds
with a little or nearly no tonal content for about 40 minutes. When they then heard the
sound with designed tones it must have seemed to be pretty loud. That the subjects'
preferred quiet sounds is also evident in the fourth part of the listening test were 16 out of
24 people voted for very quiet in the question on how they wanted a car/electric car to
sound. The second most votes were divided in four categories which all had six votes each.
The categories were sporty, with combustion noise, without combustion noise and low rumble.
47
4.6 Conclusion: Research question 3
3. Is there any point in having electric motor noise inside the passenger compartment?
Does some tonal content provide clues about the vehicle performance?
The listening test did not yield a simple answer to this research question. The test did
however provide information about the first part of the question. The subjects preferred a
sound that had a minimum of tonal-content/electric motor-noise. The latter part of the
question remains unanswered.
4.7 Conclusion: Research question 4
4. Are there similarities in the perception of the motor noise on different road surfaces,
different driving speeds and driving conditions?
The ratings of annoying were rated in similar ways for smooth and rough pavement as well
as for 50 and 80 km/h. This is seen in Figure 35 and Figure 36. The similarities when rating
annoying for constant speed and acceleration are not as clear. But the same trend is visible
Figure 33 as in Figure 35 and Figure 36, that the lowest frequency is rated less annoying.
48
5. Conclusions
The main goal of the project was to determine the level where annoyance was affected by
tonal components, expressed in PR or TNR as a function of frequency. The parameter
chosen to be used throughout the study was prominence ratio. The study shows that when a
tone is heard the sound becomes more annoying than if the tone is not heard. Therefore the
level of prominence where the tonal components affect annoyance should be the level at
which the tones are heard. A trend in the material is that frequencies below 1 kHz are
harder to detect than frequencies above 1 kHz. The lower frequency tones become audible
at values of prominence ratio between 1-4 dB. Higher frequency tones become audible at
prominence ratio 0 dB. This is not of course true for all frequencies above 1 kHz. Higher
frequencies become increasingly harder to hear with age. In this study people above 50
years of age needed a higher level to hear the test tone at 10 kHz than younger people. The
subjects older than 50 in the test may therefore have had trouble with hearing high
frequency tones that where played at a low level.
A difference between constant speed and acceleration in terms of audibility threshold for
the tones has shown to be that a lower PR is needed in order to detect a tone in the
acceleration sounds. This shows that when the tones are ramped up in frequency they are
easier to detect than when they are stationary.
The value chosen to be the limit of where the tone became audible corresponded to the PR
value where the tone was heard by at least 50% of the subjects. One disadvantage when
choosing 50% as a limit is that this value might be achieved simply by guessing yes or no if
the tone is audible.
Another aspect of how annoyance is affected by the tonal components is shown in the
ANNOVA were tones above 1 kHz are rated as more annoying than frequencies below.
These high frequency tones were also judged as less well sounding in the listening test
meaning that the subjects did not think they added anything positive in terms of
pleasantness or information to the sound. One aspect to have in mind when the tones lie
between 3-4 kHz is the equal loudness contours. They explain that these tones are
perceived slightly louder than they really are which might lead to higher annoyance since a
quiet interior sound was preferred.
One factor that could have improved the results obtained in the listening test is the scale
used in annoyance and well sounding ratings. The scale ranged from zero to ten and was
used in different ways between subjects. If information such as “a number four on
annoyance corresponds to an acceptable sound” or “a number five on annoyance is not
acceptable in a car interior sound” then further information might have been obtained and
the use of the scale might have been more consistent.
Another aspect to consider with the data from the listening test is that tones are perceived
very differently between people. The filtered sounds used in the listening test were not
completely without tonal components which can lead to people hearing other tones than
the synthesized ones. This was hopefully remedied with the listening to the sounds and the
discussion in the introduction of the listening test.
49
6. Further work
As mentioned in the limitations this study only handles one tone at the time and tones with
a constant level. Further work is therefore needed to be done on the effect of how multiple
tones are perceived. Investigating tones with varying level is also important because this is
how they are heard during acceleration. Further work could also include how the switch
frequency is perceived and in which electric/hybrid-cars and at which load cases it is heard.
50
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N. Otto, S. Amman C. Eaton, S. Lake: Guidelines for Jury Evaluations of Automotive
Sounds, SAE Technical Paper Series, 1999-01-1822, (1999).
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8. Appendix
7.1 Appendix 1: Listening test introduction text (Swedish)
Introduktion
Föreställ dig att du kör eller sitter på passagerarplatsen i en bil. Du kommer att få höra ljud
inspelade inuti bilen vid acceleration eller konstant fart. Du ska svara på om du hörde en
ton/högfrekvent pip. På denna fråga kan du kryssa antingen Ja, Osäker eller Nej. Du skall
även bedöma hur störande och hur välljudande varje ljud var med en siffra mellan 0-10. En
10:a innebär att du tyckte det var maximalt störande eller välljudande och en 0:a att det inte
var störande eller välljudande alls. Hur störande eller välljudande du bedömmer ljuden bör
grunda sig i hur du hade upplevt dem om du befann dig i en bil. Du kommer att få höra ljud
som är mellan 5 och ca 10 sekunder långa. I början av testet spelas 6 st ljudexempel för att
vänja dig vid typen av ljud som kommer att förekomma och du får då en chans att bekanta
dig med hur du ska svara. Efter de 6 st introljuden kan du ställa frågor om något är oklart.
Testet är uppdelat i 12 st "set" och kommer att ta ca 50 minuter. Mellan "set" sex och sju är
det en minuts paus då man kan plocka av hörlurarna om så önskas.
Tack för din medverkan!
7.2 Appendix 2: Listening test introduction text (English)
Introduction
Imagine that you are driving or sitting in the passenger seat of a car. You will hear sounds
recorded inside a car during constant speed or accelerating driving scenarios. Your task is
to answer whether you heard or didn’t hear a tone or a high frequency beep. This
question is answered with either Yes, Unsure or No. Additionally you should also judge
how annoying or well sounding each sound were with a number from 0-10. A number 10
means you thought the sound was maximum annoying or well sounding and a number 0
that the sound was not annoying or well sounding at all. Your ratings of annoyance and
well sounding should be based on that you were exposed to these sounds while sitting in
a car. The sounds you will here are ca 5 and 10 seconds long. In the beginning of the test
there will be 6 sound examples played in order for you to get familiar with the type of
sounds in the test and to familiarize you with how to answer the questions to each sound.
After hearing the 6 example sounds you will be able to ask questions if something is
unclear. The test is divided into 12 set and will take about 50 minutes. Between set six
and seven there will be a short brake where you can remove your headphones if you want
to.
Thank you for your participation!
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