Pages: 56-60

Abstract

Cochlear implants are being widely used for the patients with severe to profound sensori-neural hearing loss. Speech coding algorithms play an important role in improving the performance of cochlear implant. At recent years, the performance of CI has been improved for most users under the silent environment. However, as the background noise level increases, speech recognition scores are degraded considerably. In this paper, the Empirical Mode Decomposition and a selected modes approaches are applied as a speech enhancement method for cochlear implants. This algorithm is developed to extract features, called intrinsic mode functions, by a sifting process. Then, IMFs are selected based on CMSE criteria to decrease the noise effect. Also, the Choi-Williams time-frequency technique is applied to extract different components of the resulting signal. Finally, performance of this algorithm in terms of correlation analysis was compared to continuous interleaved sampling (CIS), frequency amplitude modulation encoding (FAME) and Hilbert Huang Transform Stimulating (HHTS) strategies. The results showed the highest correlation coefficient between spectrum of synthesized signal and original speech with proposed method.