International Symposium on Performance Science
ISBN 978-94-90306-02-1
© The Author 2011, Published by the AEC
All rights reserved
“Play in time, but don’t play time”: Analyzing
timing profiles in drum performances
Lorenz Kilchenmann and Olivier Senn
Lucerne University of Applied Sciences and Arts, Switzerland
This paper investigates how professional drummers intentionally vary
the micro-timing of their playing. Performances of two drummers, playing a simple rhythmic pattern in different “feels” (phrasing styles), were
recorded. The onset times of all rhythmic events were measured with
computer-aided methods, and the timing data were analyzed. Each “feel”
shows particular timing patterns. In addition, the micro-rhythmic fingerprints of the two drummers are identifiable.
Keywords: micro-timing; groove; jazz; rock; drum performance
Micro-rhythmic features are said to be an important factor in the creation and
reception of emotional qualities (“feels”) in beat-oriented musics like jazz or
rock. The ability of a drummer to play rhythmic events with flexible timing
while keeping the tempo constant is widely accepted as a sign of competence
and professionalism. “Playing ahead” or “playing laid back” are the most
common feels in the musicians’ parlance. In the first, note onsets are expected to be earlier than the beat; in the latter, note onsets are said to be later
than the beat. While these concepts are common in musicians’ conceptions of
their music, the connection between the “feels” and the physical timing features of a performance has not yet been studied.
Ethnomusicologist Charles Keil was the first to offer a procedural perspective on micro-rhythmic phenomena. According to Keil, musicians performing in an ensemble permanently synchronize their mutual timing and
adapt their intonation, dynamics, and timbre to each other. During these
negotiation processes there arise, in each analytical category, minute differences (participative discrepancies or PDs), which Keil (1966, 1987, 1995) considered to be crucial for the expressivity and emotional impact of a
performance. Keil assumed that the quality of an ensemble’s playing depended on how the conceptions of the musicians interact—particularly the
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conceptions of rhythm. In an early article (1966), Keil already observed that
“on top” playing drummers in jazz rhythm sections fit well with “chunky”
bassists (those who play with a heavy, percussive sound), whereas “laid back”
drummers are best complemented by “stringy” bassists (those who play with
a light, sustained tone). Every combination within this typology results in a
particular kind of ensemble groove. When Keil initially formulated his
thoughts, he had no access to objective timing measurement data. From the
mid-1980s on, scholars started to study temporal features of recorded performances with empirical methods (for an overview, see Pfleiderer 2006 and
Doffmann 2008) and showed that performances were patterned on a microtemporal level. Temporal PDs usually lie in a range between 10-40 ms (Rose
1989, Butterfield 2010) but can also amount to 80-90 ms in particular situations (Prögler 1995, Doffmann 2008). It remains an open question, however,
how these patterns relate to performance styles, specific performance situations, or the acquired playing habits of performers.
This study, conducted in a laboratory setting, tested how the assignment
of playing a rhythmic pattern with different feels affects the timing of two
drummers’ playing. It was hypothesized that (1) in the “ahead” feel the
strokes would generally be placed earlier than the beat, whereas in the “laid
back” feel, the strokes would be placed after the beat. It was also assumed (2)
that the timing relations between the individual instruments of the drum kit
would not change from one feel to the other because they were expected to be
based on the drummers’ acquired body motion patterns.
METHOD
Participants
For the present study, performances of two professional drummers were recorded. Drummer 1 is 42 years old and has played the drums for 32 years.
Drummer 2 is 41 and has 28 years of experience on the drums.
Procedure
The recordings took place in the participants’ private rehearsal rooms. Each
instrument of the drum kit—ride cymbal, hi-hat cymbals, snare drum, and
bass drum—was close-miked with a separate microphone. The players wore a
headset, providing a metronome click and a monitor mix of the recording.
Both musicians preferred to have the microphone signals mixed in on a very
low level in relation to the metronome click. The four microphone signals
were recorded alongside the metronome click on five separate tracks of a
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Figure 1. Rhythmic pattern used in the recordings.
digital audio workstation. The drummers played a simple 2-bar rhythm pattern (see Figure 1) for approximately three minutes in each “feel.” They had
time to memorize and practice the pattern for ten minutes prior to the recordings. The pattern in 4/4 meter featured periodical events on ride and hihat cymbals. Snare and bass drum played a mixture of metrically regular and
syncopated rhythmic elements.
The three “feels” or phrasing styles were: “ahead,” “on top,” and “laid
back.” The first is supposed to have a driving quality, the second is said to
appear on the beat, whereas the latter is expected to sound relaxed. In a
fourth recording, the drummers played the ride and hi-hat cymbal tracks
alone and tried to synchronize them as precisely as possible to the metronome.
All physical note onsets of the approximately 17,000 rhythmic events
were detected using the software LARA (www.hslu.ch/lara). For the metronome click track a threshold level of -60 dBFS was defined, and the first sample exceeding the threshold was considered as the onset of each metronome
sound. For the instrumental sounds, a two level approach was used. In a first
step, onset times were detected with an automated process. In a second step,
the resulting data was manually reviewed and compared to the waveform
plots of the recordings. Due to the close-miking, characteristic features of the
transients were clearly visible in the waveforms and served as visual cues for
the manual adjustment of the onsets. A second application of the procedure
to a subset of around 1,000 events yielded an average error of 0.1 ms for the
snare, bass drum, and hi-hat and 1 ms for the ride.
A rigid sixteenth-note reference timing grid was computed from the
measured metronome beats. For each instrumental onset time, the difference
to the corresponding time of the grid was calculated (deltaGrid). These values
represent the displacements to the external metrical reference. In addition,
the differences between hi-hat cymbals, snare, and bass drum onsets to the
ride sixteenth onsets was also computed (deltaRide). These values represent
the relative displacements from the internal metrical reference.
RESULTS
For the descriptive analysis, the deltaGrid values were used. An overview of
the results for performers 1 and 2 is given in Tables 1 and 2, respectively. The
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Table 1. Timing profile of player 1.
Ride
Hi-hat
Snare drum
Bass drum
Ahead
-22 (23)
-35 (22)
-24 (18)
-34 (19)
On top
-10 (18)
-19 (19)
-12 (17)
-20 (20)
17 (16)
5 (21)
Laid back
14 (17)
3 (21)
As precise as possible
-9 (16)
-20 (15)
Table 2. Timing profile of player 2.
Ride
Hi-hat
Snare drum
Bass drum
Ahead
-17 (13)
-22 (13)
-13 (11)
-18 (15)
On top
-19 (10)
-25 (10)
-11 (8)
-12 (11)
-3 (11)
-13 (13)
Laid back
As precise as possible
-26 (9)
5 (10)
-3 (14)
-27 (10)
Note. Mean deltaGrid values are shown, with SD in parentheses.
tables show the mean values and standard deviations of the measured deltaGrid for each instrument played in each feel. All values are shown in milliseconds, rounded to 1 ms for clarity. Positive values denote that the instrument
lagged behind the metronome in the average; negative values denote that the
strokes anticipated the metronome.
The timing profile of player 1 (Table 1) confirms the basic assumption
(hypothesis 1) very clearly. The playing instructions “ahead”/“on top”/“laid
back” appear consistently in all instruments: in the “ahead” feel, the strokes
are placed between 22 ms (ride) and 35 ms (hi-hat) earlier than the metronome clicks. Conversely, the strokes in the “laid back” feel are played between
3 ms (hi-hat) and 17 ms (snare drum) behind the metronome. The playing
instruction “on top” resulted in a rhythmic placement right between “ahead”
and “laid back,” with an anticipation between 10 ms (ride) to 20 ms (bass
drum), relative to the reference timing grid. The playing instruction “as metronomically precise as possible” resulted in mean displacements similar to
the “on top” feel. The standard deviation is slightly smaller than in the other
playing instructions. For player 1, to perfectly synchronize with the metronome means to actually play the strokes 10 ms (ride) and 20 ms (hi-hat) earlier than the perceived metronome clicks.
In player 2’s data, “ahead” and “laid back” feels are clearly differentiated.
On average, “ahead” strokes are between 9 ms (hi-hat) and 18 ms (snare
drum) earlier than “laid back” strokes. The “on top” timing is basically identi-
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cal to “ahead” timing. It is worth mentioning that drummer 2 distinguishes
the “ahead” feel from the “on top” feel essentially with dynamic means:
strokes in the “ahead” feel are generally more aggressive on the ride cymbal,
and the difference of stressed and non-stressed metrical positions is emphasized. Surprisingly, the playing instruction “as precise as possible” led to an
even stronger anticipation (26 ms) than the “ahead” feel in drummer 2. He
showed comparable standard deviations for all feel variations, but his standard deviation values are considerably lower than the values of player 1.
The examination of the differences between the individual instruments
shows characteristic patterns for both drummers, which pervade all renderings of the different feels. Drummer 1 played the foot-operated instruments
(hi-hat and bass drum) consistently 10 ms earlier than the hand-operated
instruments (ride and snare drum). Drummer 2 on the other hand led with
the hi-hat, followed by ride and bass drum; the snare drum sounded last. The
independence of these relative timing patterns from the different feels is supported by regression analyses. The variable, which encodes the “feels” is the
most important predictor variable for the deltaGrid values (ß=-0.48, t4=
-49.78, p<o.01). But it shows no influence on the deltaRide values: the feels
do not seem to influence the relative succession and timing of the strokes in
the four instruments.
DISCUSSION
The last observation supports the assumption that the relative timing between the instruments’ onsets seems to be a stable part of the drummers’
acquired motor behavior (hypothesis 2). The analysis of the data further
shows that both drummers implement the different feels in their micro-timing (thus supporting hypothesis 1), but in significantly different ways. The
players’ timing data showed diverse personal characteristics: drummer 2 had
a tendency to play ahead of the beat, regardless of the expressed feel. Based
on this characterization drummer 2 may be generally addressed as an “ahead”
drummer. For drummer 1, this label does not seem to apply: he plays the “on
top” and “ahead” feels before but the “laid back” feel after the beat. A further
predicate of jazz/rock parlance might be applicable to the two players: the
data of drummer 1 show considerable standard deviations; he seems to be a
rather “loose” drummer, who varies the placement of his strokes freely
around the metric positions. The data of drummer 2 showed noticeable
smaller standard deviations; he could be addressed as a “tighter” drummer
than his colleague, setting his strokes closer to the metric positions defined by
the metronome.
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Acknowledgments
The authors would like to thank Roland Stahl, Lucerne University of Applied Sciences
and Arts, for his consulting in statistical questions. We would also like to thank Natalie
Kirschstein for her attentive proofreading.
Address for correspondence
Lorenz Kilchenmann, Lucerne University of Applied Sciences and Arts, Zentralstrasse
18, Lucerne 6003, Switzerland; Email: [email protected]
References
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Keil C. (1987). Participatory discrepancies and the power of music. Cultural
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Keil C. (1995). The theory of participatory discrepancies: A progress report.
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Pfleiderer M. (2006). Rhythmus. Bielefeld, Germany: Transcript Verlag.
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