Subspace algorithms for noise reduction in cochlear implants

Abstract

A single-channel algorithm is proposed for noise reduction in cochlear implants. The proposed algorithm is based on subspace principles and projects the noisy speech vector onto "signal" and "noise" subspaces. An estimate of the clean signal is made by retaining only the components in the signal subspace. The performance of the subspace reduction algorithm is evaluated using 14 subjects wearing the Clarion device. Results indicated that the subspace algorithm produced significant improvements in sentence recognition scores compared to the subjects' daily strategy, at least in stationary noise. Further work is needed to extend the subspace algorithm to nonstationary noise environments.

Environment-specific noise suppression for improved speech intelligibility by cochlear implant users

“…A number of noise reduction algorithms for unilateral CI users have been proposed ͑Hochberg et al, 1992;Weiss, 1993;Yang and Fu, 2005;Loizou et al, 2005;Kasturi and Loizou, 2007;Hu et al, 2007͒. Yang and Fu ͑2005͒ tested subjects wearing the Clarion, Nucleus-22 and Med-EL devices using a spectral-subtractive noise reduction algorithm as a pre-processing step, and obtained significant improvement for recognition of speech embedded in speech-shaped noise.…”

Use of a sigmoidal-shaped function for noise attenuation in cochlear implants

“…A number of preprocessing noise-reduction algorithms have been proposed for cochlear implants over the years (Yang and Fu, 2005;Loizou et al, 2005;Loizou, 2006;Van Hoesel and Clark, 1995;Wouters and Vanden Berghe, 2001). The preprocessing approach to noise reduction, however, has three main drawbacks: (1) preprocessing algorithms sometimes introduce unwanted distortion in the signal, (2) some algorithms (e.g., subspace algorithms) are computationally complex (and consequently power hungry) and do not integrate well with existing CI strategies, and (3) it is not easy to optimize the operation of a particular algorithm to individual users.…”

A modified Wiener filtering method combined with wavelet thresholding multitaper spectrum for speech enhancement

“…Three other SE schemes, namely, wavelet thresholding (WT) [16], KLT [9], and Wiener algorithm with clean signal present (Wiener_Clean) [19], were also evaluated in order to gain a comparative analysis of the proposed SE algorithm. The KLT algorithm was proved in [1] and [22] by subjective tests to perform well in terms of preserving speech intelligibility for normal hearing listeners and improving speech intelligibility significantly for cochlear implant users in regard to recognition of sentences corrupted by stationary noises, http://asmp.eurasipjournals.com/content/2014/1/32 respectively. The Wiener_Clean algorithm was taken as the ground truth in this paper because there is clean signal used in the algorithm.…”

“…And the algorithm which performs the worst in terms of overall quality may perform well in terms of preserving speech intelligibility (e.g., KLT [9]). To our knowledge, very few speech enhancement algorithms [22][23][24][25] claimed to improve speech intelligibility by subjective tests for either normal-hearing listeners http://asmp.eurasipjournals.com/content/2014/1/32 or hearing-impaired listeners. Hence, we focused in this paper on improving performance on speech intelligibility of the SE algorithm.…”