1. 2010/12/11 Frequency Domain Blind Source Separation Based Noise Suppression to Hearing Aids (Part 1) Presenter: Cian-Bei Hong Advisor: Dr. Yeou-Jiunn Chen Date: 2010.12.1

2. 2010/12/12 Outline Introduction Purpose Literatures Review Materials & Methods Experiment Results Conclusions Future works

3. 2010/12/13 Introduction Digital hearing aids can help a user with hearing impairment to use residual hearing. –Digital hearing aids High flexibility in fittings Hearing loss compensation modulate easily Wide dynamic range compression –Improve the performance Noise reduction Acoustical source localization

4. 2010/12/14 Introduction Blind source separation (BSS) is a signal separation technology. –Independent component analysis (ICA) is a statistical approach Medical signal processing Communication signal processing Recognition system

5. 2010/12/15 Introduction Two major approaches to solving the BSS problem. –Time domain Convolution algorithm Good results for instantaneous mixtures –Frequency domain Multiplication algorithm Permutation and scaling

6. 2010/12/16 Purpose In light of the binaural hearing aids, based on frequency domain blind source separation to develop noise suppression techniques.

7. 2010/12/17 Literatures Review (1/2) Abstract –A hearing aid can help the hearing impaired to listen to speech by amplifying its sound Fig. 1. Block diagram of the proposed method “New idea of hearing aid algorithm to enhance speech discrimination in a noisy environment and its experimental results”, 2004

8. 2010/12/18 Literatures Review (1/2) Fig. 2. Speech, noise, input and output signals “New idea of hearing aid algorithm to enhance speech discrimination in a noisy environment and its experimental results”, 2004

9. 2010/12/19 Literatures Review (2/2) Abstract –Using eight microphones separate a mixture of six speech signals arriving from various directions Fig. 3. Room layout for experimentsFig. 4. Solving ambiguity of estimated DOAs “Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models”, 2006

10. 2010/12/110 Literatures Review (2/2) Fig. 5. Permutation solved by using DOAs “Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models”, 2006

11. 2010/12/111 Reference [1] S. M. Lee, J. H. Won, S. Y. Kwon, Y. C. Park, I. Y. Kim, S. I. Kim, “New idea of hearing aid algorithm to enhance speech discrimination in a noisy environment and its experimental results,” International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS ’04), pp. 976-978, 2004 [2] R. Mukai, H. Sawada, S. Araki, S. Makino, “Frequency Domain Blind Source Separation of Many Speech Signals Using Near-field and Far-field Models,” EURASIP Journal on Applied Signal Processing, vol. 2006, pp. 1-13, 2006 [3] A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, John Wiley & Sons, Inc, 2001 [4] J. F. Cardoso, “High-order contrasts for independent component analysis,” Neural Computation, vol. 11, pp. 157-192, 1999 [5] H. Sawada, R. Mukai, S. Araki, and S. Makino, “A robust and precise method for solving the permutation problem of frequency-domain blind source separation,” IEEE Transactions on Speech and Audio Processing, vol. 12, no. 5, pp. 530-538, 2004