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Hearing loss and sparse coding

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

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Hearing loss and sparse coding

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  1. Hearing loss and sparse coding Stefan Bleeck, Institute of Sound and Vibration Research, Hearing and Balance Centre University of Southampton

  2. Question: • Can sparse coding help to overcome problems caused by hearing loss? • overview of the hearing process • Examples of sparse algorithms for hearing aids and cochlear implants • Preliminary results bleeck@gmail.com

  3. a short journey into Hearing bleeck@gmail.com

  4. The outer ear Important for sound localization, linear => boring bleeck@gmail.com

  5. tympanum (middle ear) Important to explain limits of hearing, linear => boring bleeck@gmail.com

  6. the inner ear and the vestibular system • Contained within bony labyrinth in temporal bone • Cochlea does hearing • Semicircularcanals+utricle does balance • Same mechanism, nerve, evolution, similar problems bleeck@gmail.com

  7. frequency mapping bleeck@gmail.com 7

  8. Stereocilia • Stereocilia detect vibrations within cochlea. • Introduce half-wave rectification • Nonlinear bleeck@gmail.com 8

  9. range of loudness 10 1 10-2 10-4 13 orders of magnitude 10-6 10-8 10-10 10-12 bleeck@gmail.com

  10. ABSOLUTE THRESHOLD CURVE Threshold as function of Frequency membranemoves 10-13 m bleeck@gmail.com

  11. Cochlear nonlinearities • Amplitude (nonlinear amplification) • Frequencies (combination tones) • compression Demo: sweeps bleeck@gmail.com 11

  12. Active Processes healthy “active” OHCs provide up to 40 dB amplification(= factor of 100) Travelling Wave Envelope on Basilar Membrane due to Pure Tone Stimulus: damaged “passive” BM displacement (nm) Distance along BM (mm) OHCs inject energy in this region

  13. hearing loss (noise induced and presbyacousis) • 50% of 60 year old, 90% of 80 year old • Hearing aids are not good enough • ‘damage’ €2.4 Billion per year in EU • Lack of research funding today bleeck@gmail.com 13

  14. Hair cell loss by noise exposure Electron micrographs of cochlear hair cells. Left: healthy, right: damaged by noise exposure. bleeck@gmail.com 14

  15. Normal and impairedAuditory filter shapes • Hearing impairment • loosing audibility, • Also widening of filter • both results in difficulties to understand language, especially in noise bleeck@gmail.com 15

  16. The challenge

  17. Listening in noise Hearing Impaired 100% Aided Un-aided Normal Word recognition ASR 50% 0% 0 dB -15 dB 40 dB SNR

  18. bleeck@gmail.com

  19. ‘standard’ methods of denoising bleeck@gmail.com

  20. What 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 information • Extract content based on Information not on Energy • Specifically speech related information bleeck@gmail.com

  21. Bio-inspired approach to denoising speech 3 Denoising (sparsification) 2 Neural representation: (Transformation) 1 periphery model

  22. ‘Sparse’ algorithms developed in our group noise bleeck@gmail.com

  23. Filter bank bleeck@gmail.com

  24. Example: nmf • Non-negative matrix factorization • Matrix 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: g(H)= regularity function λ= sparsity factor bleeck@gmail.com

  25. Iterative algorithm to minimize the cost function by gradient decent: λ depends on SNR because of trade-off intelligibility - quality low noise: no sparsification high noise: lots Task: fine out how! Online experiment (restricted by speed of hardware) Offline experiment (unrestricted) bleeck@gmail.com

  26. For ‘bin’, ‘pin’, ‘din’, ‘tin’ W Z H bleeck@gmail.com

  27. bleeck@gmail.com

  28. On-line experimental set up: bleeck@gmail.com

  29. Sound examples: • 22 channel filter bank • 16 msframes • Gaussian noise SNR=5 dB clean noisy frequency denoised time

  30. Results from CI listeners in online experiment (problems with iteration!) bleeck@gmail.com

  31. Results from CI listeners in offline experiment results for all participants Best sparsification as function of snr: Averaged bleeck@gmail.com

  32. Conclusions: • Sparse coding can help reduce acoustic information in a useful way • Development still in its infancy, hardware restrictions still relevant • High impact research field with lots of potential funding • Strength of our group: clinical evaluation, • weakness at the moment: lack of signal processing experts

  33. Hu, 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), 75–82. doi:10.3109/14992027.2011.625984 • Sang, 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. 1–5). bleeck@gmail.com

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