Psychoacoustically-motivated adaptive β-order generalized spectral subtraction for cochlear implant patients

Many cochlear implant (CI) users are able to understand speech in quiet listening conditions, however, CI users´ speech recognition deteriorates rapidly as the level of background noise increases. To make CI more applicable in reallife environments, noise reduction is needed in CI processor. Recently, we presented a psychoacoustically-motivated adaptive beta-order generalized spectral subtraction (GSS) which deals with the weakness of the traditional SS algorithms. To apply this adaptive beta-order GSS into CI processor, in this paper, we investigate the effects of noise estimation approaches and residual noise components for the proposed adaptive beta-order GSS. Word-in-sentence recognition in steady white noise and speech babble noise was measured in four CI users. Experimental results showed that 1) noise estimation significantly affected performance of the proposed algorithm, 2) the algorithm with the least residual noise components was preferred by CI subjects, and 3) the proposed psychoacoustically-motivated adaptive beta-order GSS outperformed the traditional SS algorithms.