Hearing loss and sparse coding. Stefan Bleeck, Institute of Sound and Vibration Research, Hearing and Balance Centre University of Southampton . Question:. Can sparse coding help to overcome problems caused by hearing loss? overview of the hearing process - PowerPoint PPT Presentation
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Hearing loss and sparse codingStefan Bleeck, Institute of Sound
and Vibration Research, Hearing and Balance CentreUniversity of
Southampton
Can sparse coding help to overcome problems caused by hearing
loss?
overview of the hearing processExamples of sparse algorithms for
hearing aids and cochlear implantsPreliminary results
5/11/[email protected]:a short journey into
Hearing
5/11/[email protected]
The outer ear5/11/[email protected] for sound
localization, linear => boring4tympanum (middle
ear)5/11/[email protected]
Important to explain limits of hearing, linear =>
boring55/11/[email protected] inner ear and the vestibular
system
Contained within bony labyrinth in temporal boneCochlea does
hearingSemicircular canals+utricle does balanceSame mechanism,
nerve, evolution, similar problems
5/11/[email protected]
frequency mapping
5/11/[email protected] detect
vibrations within cochlea.Introduce half-wave
rectificationNonlinear
[email protected] of loudness
13 orders of magnitude10Power
(watts/m2)10-1210-210-410-610-810-10195/11/201210
ABSOLUTE THRESHOLD CURVEmembrane moves 10-13 mThreshold as
function of
[email protected]/11/[email protected]
nonlinearitiesAmplitude (nonlinear amplification)Frequencies
(combination tones)compression
Demo: sweeps
12Active ProcessesDistance along BM (mm)BM displacement
(nm)damaged passivehealthy activeOHCs inject energy in this region
OHCs provide up to 40 dB amplification(= factor of 100)Travelling
Wave Envelope on Basilar Membranedue to Pure Tone Stimulus:50% of
60 year old, 90% of 80 year oldHearing aids are not good
enoughdamage 2.4 Billion per year in EULack of research funding
today5/11/[email protected] loss (noise induced and
presbyacousis)
5/11/[email protected] cell loss by noise
exposure
Electron micrographs of cochlear hair cells. Left: healthy,
right: damaged by noise exposure.
5/11/[email protected] and impairedAuditory filter
shapes
Hearing impairment loosing audibility, Also widening of filter
both results in difficulties to understand language, especially in
noise
15The challenge
16Listening in noise0 dB40 dB-15 dBASRNormalHearing
ImpairedSNRWord
recognition100%0%AidedUn-aided50%5/11/[email protected]
185/11/[email protected] methods of denoising
5/11/[email protected] we think a better solution could
be:Problem: Hearing loss constitutes a bottle neck: not all
information can get through
Solution: extract less, but important informationExtract content
based on Information not on Energy Specifically speech related
information
Bio-inspired approach to denoising speech
2 Neural representation: (Transformation)3 Denoising
(sparsification)1 periphery modelNeural model Based on the cochlear
nucleus215/11/[email protected]
Sparse algorithms developed in our
groupnoise5/11/[email protected]
Filter bank
Non-negative matrix factorizationMatrix Z is factorised into two
non-negative matrices W and H (basis vectors (5) and activity over
time)(motivated by the processing in CI and auditory neurons)Z here
is the envelopegram (22 channels, 128 pt)Factorization using
Euclidean cost function:
Sparseness constrained:
5/11/[email protected]: nmf
g(H)= regularity function= sparsity factor
Iterative algorithm to minimize the cost function by gradient
decent:
depends on SNRbecause of trade-off intelligibility - qualitylow
noise: no sparsificationhigh noise: lots
Task: fine out how!
Online experiment (restricted by speed of hardware) Offline
experiment
(unrestricted)5/11/[email protected]/11/[email protected]
For bin, pin, din, tinZWH5/11/[email protected]
5/11/[email protected]
On-line experimental set up:Sound examples:22 channel filter
bank 16 ms framesGaussian noiseSNR=5 dB
cleannoisydenoisedtimefrequencyNeurons just follow energy in in
signal295/11/[email protected]
Results from CI listeners in online experiment(problems with
iteration!)5/11/[email protected]
Results from CI listeners in offline experiment
results for all participantsAveragedBest sparsification as
function of snr:Conclusions:Sparse coding can help reduce acoustic
information in a useful wayDevelopment still in its infancy,
hardware restrictions still relevantHigh impact research field with
lots of potential funding Strength of our group: clinical
evaluation, weakness at the moment: lack of signal processing
experts
5/11/[email protected], H., Li, G., Chen, L., Sang, J.,
Wang, S., Lutman, M. E., & Bleeck, S. (2011). Enhanced sparse
speech coding strategy for cochlear implants. European Signal
Processing Conference (EUSIPCO).Hu, H., Taghia, J., Sang, J.,
Taghia, J., Mohammadiha, N., Azarpour, M., Dokku, R., et al.
(2011). Speech Enhancement via Combination of Wiener Filter and
Blind Source Separation. International Conference on Intelligent
Systems and Knowledge Engineering. Sang, J., Hu, H., Li, G.,
Lutman, M. E., & Bleeck, S. (2011a). Application of a sparse
coding strategy to enhance speech perception for hearing aid users.
British Society of Audiology Short Papers Meeting.Sang, J., Hu, H.,
Li, G., Lutman, M. E., & Bleeck, S. (2011b). Enhanced Sparse
Speech Processing Strategy in Cochlear Implants. Conference on
implantable Auditory Prostheses (CIAP).Sang, J., Li, G., Hu, H.,
Lutman, M. E., & Bleeck, S. (2011a). Supervised Sparse Coding
in Cochlear Implants. Conference on implantable Auditory Prostheses
(CIAP).Sang, J., Li, G., Hu, H., Lutman, M. E., & Bleeck, S.
(2011b). Supervised Sparse Coding Strategy in Hearing Aids. Annual
Conference of the International Speech Communication Association
(INTERSPEECH).Bleeck, S., Wright, M. C. M., & Winter, I. M.
(2012). Speech enhancement inspired by auditory modelling.
International Symposium on Hearing.Hu, H., Mohammadiha, N., Taghia,
J., Leijon, A., Lutman, M. E., Bleeck, S., & Wang, S. (2012).
Sparsity Level in a Non-negative Matrix Factorization Based Speech
Strategy in Cochlear Implants. EUSIPCO.Li, G, Lutman, M. E., Wang,
S., & Bleeck, S. (2012). Relationship between speech
recognition in noise and sparseness. International Journal of
Audiology, 51(2), 7582. doi:10.3109/14992027.2011.625984Sang, J.,
Hu, H., Zheng, C., Li, G., Lutman, M. E., & Bleeck, S. (2012).
Evaluation of a Sparse Coding Shrinkage Algorithm in Normal Hearing
and Hearing Impaired Listeners. EUSIPCO (pp.
15).Chart1-45-8-34-7-25-4-15-2-5-100-5-4-18-10-30-18-40-25-50-30
NormalImpairedFrequency (kHz)Filter gain (dB)
Filter shapesAuditory filter
shapesFrequencyNormalImpaired0.5-45-80.6-34-70.7-25-40.8-15-20.9-5-11001.1-5-41.2-18-101.3-30-181.4-40-251.5-50-30
