SLIDE 1 Modelling and signal processing of gap detection process
Willy Wong, University of Toronto
SLIDE 2 Acknowledgements
Hilmi Dajani (assoc prof, U of Ottawa)
- Method of significance
- Dr. Jie Cui (Barrow Neurological Institute)
- Neural modelling
Ewen Macdonald (assoc prof, TU Denmark) Sheena Luu
SLIDE 3
Gap detection
f1 f2 t stimulus envelope “seen” by ear
SLIDE 4
Gap in speech perception
Gaps in speech signals can cue phonetically important distinctions; for example, a gap is one cue to the presence of the stop consonant phoneme /p/ in the word “spoon,” in contrast to the word “soon” in which there is no gap. Pichora-Fuller et al (2006)
SLIDE 5 Single-channel model
Model of single-channel gap detection BM IHC AN sound towards central regions
SLIDE 6
Model response
sound BM IHC AN
SLIDE 7
Across channels
Formby et al (1998) Across-channel gap detection Small frequency: single-channel detection Big frequency: between-channel detection However, comprehensive model is missing
SLIDE 8 Electrophysiological approaches
- e.g. Shinohara et al (1995)
- correlation of ABR wave
V to gap threshold
- EP threshold < PP threshold
We will be using an EP approach
SLIDE 9 Motivation
- Electrophysiology is informative but
challenging to interpret/analyze
- Comprehensive model still missing
- Hopefully there is a role for me here!
SLIDE 10 Outline
- Time-frequency analysis
- Blind statistical technique
- Theory and modelling
SLIDE 11
Time-frequency analysis
SLIDE 12 Frequency analysis and stationary signals
time domain frequency domain
SLIDE 13 Non-stationary signals
Plotting spectral distribution over time is call a spectrogram
General area is called time-frequency analysis
SLIDE 14 Time-frequency analysis can applied to a variety of signals
SLIDE 15 Speech spectrogram
e.g. Winckel, Music, Sound and Sensation
SLIDE 16
Importance for formant analysis
e.g. for vowels
SLIDE 17 TF also good for EP data!
e.g. Farzan et al 2010
SLIDE 18
We did a relevant project involving time- frequency analysis of speech & ABR
SLIDE 19
Auditory brainstem response
“click”
SLIDE 20
Periphery Brain
SLIDE 21
This is all fine if we are interested in “clicks”. But what about real signals like speech?
SLIDE 22
Clinical audiometry
+ =
normal abnormal click
SLIDE 23
Complex signal ABR
+ = ABR TF ?
normal abnormal e.g. speech
SLIDE 24 EP follows pitch contour
IEEE Transactions of Biomedical Engineering, 2005
SLIDE 25 Live demonstration
voice voice
(hpf 300Hz)
spectrogram spectrogram
abr abr
experiment
SLIDE 26 Speech ABR
Pitch track Evoked response Evoked response (high-passed)
Repeated by other investigators e.g. cABR, Kraus (2010)
SLIDE 27
Research question
We can see complex stimulus in evoked response (in MEG as well?) Similarly can we see “gap” in response? If so, we can use to same method to study threshold!
SLIDE 28
Method of significance
SLIDE 29
Motivation
What if we don’t know where to look? Here is a “blind technique” that can help you
SLIDE 30
Neural signals are noisy
task A task B
SLIDE 31 Method of significance
e.g. Talakoub et al 2013
SLIDE 32 Typically xi is power. X : SX = {x1, x2, ..., xN} X is chi-squared distributed. The transformation Y = 10 log10(X) gives a normal distribution. T-test can be applied. Test of significance can be used to find regions of difference in time-frequency space.
Theory
SLIDE 33 t-test
task A task B
SLIDE 34
Many investigators explore the P1-N1-P2 complex (50-200 ms after stimulus) for gap detection. Could there be better correlates to threshold? Method of significance can be used to find out!
Research questions
SLIDE 35
Theory and modelling
SLIDE 36 Aim
Can we have a comprehensive understanding of gap detection thresholds?
SLIDE 37 Review
- F1≃ F2: in-channel (peripheral processing)
- F1≠ F2: between-channel (central processing)
Need: model of central processing of sound
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SLIDE 42 Research questions
- Simulate peripheral neural response for gap
detection
- Extend method to between-channel gap
detection in other domains (speech, localization, etc)
- Can study relative timing between channels
SLIDE 43
Conclusions
We hope some of these tools will be useful for project Happy to collaborate on data analysis and model development!
SLIDE 44
Questions?
