A Robust Dual-Microphone Generalized Sidelobe Canceller Using a Bone-Conduction Sensor for Speech Enhancement

The use of multiple spatially distributed microphones allows performing spatial filtering along with conventional temporal filtering, which can better reject the interference signals, leading to an overall improvement of the speech quality. In this paper, we propose a novel dual-microphone generalized sidelobe canceller (GSC) algorithm assisted by a bone-conduction (BC) sensor for speech enhancement, which is named BC-assisted GSC (BCA-GSC) algorithm. The BC sensor is relatively insensitive to the ambient noise compared to the conventional air-conduction (AC) microphone. Hence, BC speech can be analyzed to generate very accurate voice activity detection (VAD), even in a high noise environment. The proposed algorithm incorporates the VAD information obtained by the BC speech into the adaptive blocking matrix (ABM) and adaptive noise canceller (ANC) in GSC. By using VAD to control ABM and combining VAD with signal-to-interference ratio (SIR) to control ANC, the proposed method could suppress interferences and improve the overall performance of GSC significantly. It is verified by experiments that the proposed GSC system not only improves speech quality remarkably but also boosts speech intelligibility.

Generalized Sidelobe Canceller Beamforming with Combined Postfilter and Sparse NMF for Speech Enhancement

A multi-microphone array speech enhancement method using Generalized Sidelobe Canceller (GSC) beamforming with Combined Postfilter (CP) and Sparse Non-negative Matrix Factorization (SNMF) is proposed in this paper. GSC beamforming with CP and SNMF is implemented to reduce directional noise, diffuse noise, residual noise and to separate interferences in adverse environment. In this paper, the directional noise is reduced using GSC beamforming, whereas the diffuse noise in each subband is reduced with a combined postfilter using Unconstrained Frequency domain Normalized Least Mean Square (UFNLMS) algorithm. Finally, the residual noise at the output of CP is eliminated by SNMF which optimizes the noise. The performance of the proposed method is evaluated using parameters like PESQ, SSNR, STOI, SDR and LSD. The noise reduction for four and eight microphones is compared and illustrated in spectrograms. The proposed method shows better performance in terms of intelligibility and quality when compared to the existing methods in adverse environments.

Improved the Efficiency of Generalized Sidelobe Canceller Algorithm by using Speech Presence Probability

Speech signal processing application always encounter certain difficulties in real complex environment. The captured signal on microphones often interfered by coherent, incoherent, stationary, non-stationary noise and self acoustic mismatch. To solve this problem, the necessary requirement is speech enhancement to extract target speaker from observed signals in condition minimum speech distortion, while removing background noise. The author proposed a speech enhancement generalized sidelobe canceller based on an estimation of speech presence probability. Main ideal of the algorithm is accuracy estimation of auto and cross power spectral densities of main and reference signal, which used in process of filtering. The experimental result ensures the effectiveness of the proposal algorithm, the background noise is suppressed while the quality of speech is improved in compared with the conventional generalized sidelobe canceller. The proposed algorithm can be evaluated as a frontend for automatic speech application.