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.

This paper proposes a new technique for improving a generalized sidelobe canceller (GSC) for dual-microphone speech enhancement to be applied in an auditory device such as a hearing aid. Here, the GSC is implemented on a 32-channel uniform polyphase discrete Fourier transform filter bank, where the overall algorithm processing delay is 8 ms to meet hearing aid requirements. The proposed method can improve the fixed beamformer (FBF) and control the adaptive algorithm in the noise canceller (NC) using the phase difference obtained from dual-microphone signals. For this, spatial cues such as the phase differences are used to estimate the target-to-non-target directional signal ratio (TNR). A target-directional speech enhancing spectral gain-attenuator is calculated based on the estimated TNR, which is then incorporated to improve the FBF in the GSC. Furthermore, the weight update of the adaptive NC in the GSC is formulated using the phase difference-based TNR. The experimental results show that the auditory speech enhancement system that employs the proposed dual-microphone GSC algorithm provides better perceptual quality and intelligibility scores than conventional methods such as a beamformer, phase-error-based filter (PEF), GSC, or PEF-controlled GSC under multiple noise conditions of signal-to-noise ratio range 0-20 dB.

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