Abstract
This paper proposes a novel technique to improve a spectral statistical filter for speech enhancement, to be applied in wearable hearing devices such as hearing aids. The proposed method is implemented considering a 32-channel uniform polyphase discrete Fourier transform filter bank, for which the overall algorithm processing delay is 8 ms in accordance with the hearing device requirements. The proposed speech enhancement technique, which exploits the concepts of both non-negative sparse coding (NNSC) and spectral statistical filtering, provides an online unified framework to overcome the problem of residual noise in spectral statistical filters under noisy environments. First, the spectral gain attenuator of the statistical Wiener filter is obtained using the a priori signal-to-noise ratio (SNR) estimated through a decision-directed approach. Next, the spectrum estimated using the Wiener spectral gain attenuator is decomposed by applying the NNSC technique to the target speech and residual noise components. These components are used to develop an NNSC-based Wiener spectral gain attenuator to achieve enhanced speech. The performance of the proposed NNSC–Wiener filter was evaluated through a perceptual evaluation of the speech quality scores under various noise conditions with SNRs ranging from -5 to 20 dB. The results indicated that the proposed NNSC–Wiener filter can outperform the conventional Wiener filter and NNSC-based speech enhancement methods at all SNRs.
Highlights
Individuals with hearing impairment often have trouble understanding the specific meaning of speech in their everyday lives
The limitations associated with noisy speech in the context of hearing aids were reported more than 35 years ago [4] and have not yet been effectively addressed
Part of the noise spectrum can be represented by the speech dictionary, and it generates a certain residual noise in the estimated target speech. Considering these aspects, this paper proposes an improved non-negative sparse coding (NNSC)-based speech enhancement algorithm that reduces the residual noise based on the principle that the residual noise components remaining after processing through the DD-based Wiener filter tend to be whitened [9,10]
Summary
Individuals with hearing impairment often have trouble understanding the specific meaning of speech in their everyday lives. Many individuals find the functioning of hearing aids to be inadequate, mostly owing to the interference of noise with the speech signal entering the ear. Of hearing-impaired (HI) (All the abbreviations used in this paper are listed in the Abbreviations) individuals use hearing aid devices [2,3]. The limitations associated with noisy speech in the context of hearing aids were reported more than 35 years ago [4] and have not yet been effectively addressed. A potential solution is to use multiple microphones, which can improve the signal-to-noise ratio (SNR); this improvement is limited by several factors. In real-life situations, hearing aids cannot function adequately in environments involving multiple noise sources and high reverberation [5]
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