Nonnegative Matrix Factorization Sparse Coding Strategy for Cochlear Implants

Abstract

AbstractWith the development of new speech processors and algorithms, the majority of cochlear implant (CI) users benefit from their device, however, the average performance of most CI users still falls below normal hearing (NH) listeners, and speech quality and intelligibility generally deteriorate in the presence of background noise. Cochlear implants require efficient speech processing to maximize information transfer to the brain, especially in noise. Our current work is to improve the performance of CIs in noisy environments by developing new speech processing strategies. In this chapter, a nonnegative matrix factorization (NMF)-based speech coding strategy is introduced, where the speech is first transferred to the time–frequency domain via a 22-channel filter bank and the envelope in each frequency channel is extracted; and then the NMF SPARSE strategy is applied on these envelopes. The algorithm was evaluated by objective and subjective experiments, and the results were compared to the standard CI speech processing strategy (Advanced Combination Encoder, ACE). A vocoder simulation study with six participants showed that the proposed sparse NMF strategy can outperform ACE, especially at low SNR for both speech intelligibility and quality.KeywordsCochlear ImplantNonnegative Matrix FactorizationSpeech IntelligibilityClean SpeechNoisy SpeechThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.