Cross-Correlations and
Cleaning Up Data
Jessica Ferguson
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Senior Computer Science Major
English: Creative Writing Minor
Pacific University
Project Aims: HSD Project
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Aim 1: Collecting and transcribing spoken
language data
Aim 2: Automatically deriving features from
spoken language samples
Aim 3: Characterizing features derived from
Aim 2
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My Task
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Falls under Aim 1
Improving the quality of the recordings in the
corpus
Reducing noise to give clearer speech
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Subjects
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Currently enrolled in studies at the Layton Aging
and Alzheimer’s Disease Center at OHSU
Individuals over 90
 Individuals with Mild Cognitive Impairment (MCI)
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Test Battery
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Wechsler Logical Memory I/II (Story Recall)
Category Fluency (Fruits, States)
Picture Description Task
Autobiographical reflections
Conversational Speech
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Recording Setup
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Same for all sessions
Four different microphones set up
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Tests administered by examiner
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Characteristics of Recordings
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Similarly-shaped waves
Shifted horizontally
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Sample Waves
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Shifting Files
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Shifting files is relatively easy
But how far to shift?
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Close-up of Comments Files
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Observed shift:
380
 320
 315
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Calculating Shift – Cross-Correlation
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Cross-correlation: a measure of how similar one
signal is to another
To calculate: split the file into overlapping windows
 Take windows of the same length in another file
 Multiply them together
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Cross-Correlation Cont.
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The window we multiply it by in the other file
keeps getting moved by one sample (1/16 msec)
If corresponding values have the same sign, they
contribute positively
If one is negative and the other is positive, they
contribute negatively
We take the highest value from the range
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Issues with Cross-Correlation
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With original parameters:
Window length: 1280 samples
 Lag: -400 to 400 samples
 For one value: 1280 * 800 = 512,000
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One value every 10 msec: 100 values per second
of file correlated
This gets unmanageable very quickly
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Time Under Original Parameters
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Correlate 1.5s of files: up to 20 minutes
Relatively high accuracy, but impractical
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Task: Reduce time while maintaining accuracy
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Optimizing Parameters
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Parameters that could be adjusted:
Window Size
 Lag
 Number of correlations (how much of the file gets
correlated)
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Window Size
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Initial parameters were 200 msec
Decreasing below 80 msec resulted in unacceptable
loss of accuracy
 Runtime was improved but not significantly enough
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Number of Correlations
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Unfortunately, correlations are not always
perfect
We take the mode of the correlations produced
n = 150 was the minimum, and still had a high
error rate
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Lag
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Recall the sound wave images from before:
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Lag cont.
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Assume that these are representative
Lag values should all be between 300-400
samples (18-25 msec)
Add this to previous improvements:
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Runtime for one set of four files decreases to about
5-6 min
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Other Benefits
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If the assumption holds:
Error from optimal value decreases
 Max. error decreases from 50 msec to 6msec
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Original File
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Taken from a picture description task
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Shifted File
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The same file, but correlated and shifted
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Acknowledgements
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Paul Hosom and Brian Roark
Fellow Interns
Everyone who has made me welcome at CSLU
Questions?