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Digital Signal Processing Algorithms for Noise Reduction, Dynamic Range Compression, and Feedback Cancellation in Hearing Aids

Hearing loss can be caused by many factors, e.g., daily exposure to excessive noise in the work environment and listening to loud music. Another important reason can be age-related, i.e., the slow loss of hearing that occurs as people get older. In general hearing impaired people suffer from a frequency-dependent hearing loss and from a reduced dynamic range between the hearing threshold and the uncomfortable level. This means that the uncomfortable level for normal hearing and hearing impaired people suffering from so called sensorineural hearing loss remains the same but the hearing threshold and the sensitivity to soft sounds are shifted as a result of the hearing loss. To compensate for this kind of hearing loss the hearing aid should include a frequency-dependent and a level-dependent gain. The corresponding digital signal processing (DSP) algorithm is referred to as dynamic range compression (DRC). Background noise (from competing speakers, traffic etc.) is also a significant problem for hearing impaired people who indeed have more difficulty understanding speech in noise and so in general need a higher signal-to-noise-ratio (SNR) than people with normal hearing. Becauseof this the noise reduction (NR) is also an important algorithmic component in hearing aids. Another issue in hearing aids is the undesired acoustic coupling between the loudspeaker and the microphone which is referred to as the acoustic feedback problem. Acoustic feedback produces an annoying howling sound and limits the maximum amplification that can be used in the hearing aid without making it unstable. To tackle the acoustic feedback problem adaptive feedback cancellation (AFC) algorithmsare used. Acoustic feedback is becoming an even more significant problem due to the use of open fittings and the decreasing distance between the microphone and the loudspeaker.

In this thesis several DSP techniques are presented to address the problems introduced above. For the background noise problem, we propose a NR algorithm based on the speech distortion weighted multi-channel Wiener filter (SDW-MWF) that is designed to allow for a trade-off between NR and speechdistortion. The first contribution to the SDW-MWF based NR is based on using a weighting factor that is updated for each frequency and for each frame such that speech dominant segments and noise dominant segments can be weighted differently. This can be done by incorporatingthe conditional speech presence probability (SPP) in the SDW-MWF. The second contribution is based on an alternative and more robust method to estimate and update the correlation matrices, which is very important since an SDW-MWF based NR is uniquely based on these correlation matrices.The proposed SDW-MWF based NR shows better performance in terms of SNR improvement and signal distortion compared to a traditional SDW-MWF.

For the problem of background noise and reduced dynamic range, we propose a combined algorithm of an SDW-MWF based NR and DRC. First the DRCis extended to a dual-DRC approach that allows for a switchable compression characteristic based on the conditional SPP. Secondly the SDW-MWF incorporating the conditional SPP is combined and analysed together with the dual-DRC. The proposed method shows that the SNR degradation can be partially controlled by using the dual-DRC.

For the acoustic feedback problem, we propose a prediction error method based AFC (PEM-based AFC) exploiting an improved cascaded near-end signal model. The challenge in PEM-based AFC is to accurately estimate the near-end signal model such that the inverse of this model can be used as a decorrelation of theloudspeaker and the microphone signals. Due to the closed signal loop the loudspeaker and the microphone signal are now correlated which causesstandard adaptive filtering methods to fail. The proposed PEM-based AFC shows improved performance in terms of maximum stable gain (MSG) and filter misadjustment compared to a PEM-based AFC using a single near-end signal model.

Date:10 Jan 2008  →  8 Jul 2011
Keywords:Hearing aids, Signal processing
Disciplines:Laboratory medicine, Palliative care and end-of-life care, Regenerative medicine, Other basic sciences, Other health sciences, Nursing, Other paramedical sciences, Other translational sciences, Other medical and health sciences
Project type:PhD project