Supervised sparse coding strategy in cochlear implants

In this paper we explore how to improve a sparse coding (SC) strategy that was successfully used to improve subjective speech perception in noisy environment in cochlear implants. On the basis of the existing unsupervised algorithm, we developed an enhanced supervised SC strategy, using the SC shrinkage (SCS) principle. The new algorithm is implemented at the stage of the spectral envelopes after the signal separation in a 22-channel filter bank. SCS can extract and transmit the most important information from noisy speech. The new algorithm is compared with the unsupervised algorithm using objective evaluation for speech in babble and white noise (signal-to-noise ratios, SNR = 10dB, 5dB, 0dB) using objective measures in a cochlea implant simulation. Results show that the supervised SC strategy performs better in white noise, but not significantly better with babble noise.