Sparsity level in a non-negative matrix factorization based speech strategy in cochlear implants

Non-negative matrix factorization (NMF) has increasingly been used as a tool in signal processing in the last years, but it has not been used in the cochlear implants (CIs). To improve the performance of CIs in noisy environments, a novel sparse strategy is proposed by applying NMF on envelopes of 22 channels. In the new algorithm, the noisy speech is first transferred to the time-frequency domain via a 22- channel filter bank and the envelope in each frequency channel is extracted; secondly, NMF is applied to the envelope matrix (envelopegram); finally, the sparsity condition is applied to the coefficient matrix to get more sparse representation. Speech reception threshold (SRT) subjective experiment was performed in combination with five objective measurements in order to choose the proper parameters for the sparse NMF model.