A novel speech coding algorithm for cochlear implants

Cochlear implants (CI) can restore some degree of hearing to individuals with severe to profound sensorineural hearing loss. In recent years, new speech coding algorithms were developed for improving the performance of cochlear implants, but sound recognition in noisy environment, tonal language and music perception remain very difficult for most cochlear implant users. To enhance speech recognition in noise, as well as tonal language and music perception, a new speech coding algorithm called Hilbert Huang Transform Stimulating(HHTS) for cochlear implants was presented. HHT is a powerful tool which consists of sifting procedure of empirical mode decomposition (EMD) and the Hilbert Transform (HT) to analyze non-linear and non-stationary signal. Instantaneous frequency could be derived from time-frequency description of speech signal in the sifting procedure and a lot of information comprised in fine structure is not only reflection of speech contents, speech rhythms and tones, but also speakers´ individual characteristics, so that have to get finer envelope and fine structure properties of speech. HHTS, continuous interleaved sampling (CIS), channel specific sampling sequences (CSSS), frequency amplitude modulation encoding (FAME) strategies were simulated based on MATLAB. Synthesized stimulus and their spectrum were correlation analyzed between original signals. Compared to other 3 strategies, HHTS obtain the highest correlation coefficient between spectrum of synthesized signal and that of original speech. The spectrum of synthesized signal through HHTS strategy is the most correlated to that of original speech, and the correlation is significant.