Abstract
Speech enhancement is one of the most important fields in audio and speech signal processing. The speech enhancement methods are divided into the single and multi-channel algorithms. The multi-channel methods increase the speech enhancement performance by providing more information with the use of more microphones. In addition, spatial aliasing is one of the destructive factors in speech enhancement strategies. In this article, we first propose a uniform circular nested microphone array (CNMA) for data recording. The microphone array increases the accuracy of the speech processing methods by increasing the information. Moreover, the proposed nested structure eliminates the spatial aliasing between microphone signals. The circular shape in the proposed nested microphone array implements the speech enhancement algorithm with the same probability for the speakers in all directions. In addition, the speech signal information is different in frequency bands, where the sub-band processing is proposed by the use of the analysis filter bank. The frequency resolution is increased in low frequency components by implementing the proposed filter bank. Then, the affine projection algorithm (APA) is implemented as an adaptive filter on sub-bands that were obtained by the proposed nested microphone array and analysis filter bank. This algorithm adaptively enhances the noisy speech signal. Next, the synthesis filters are implemented for reconstructing the enhanced speech signal. The proposed circular nested microphone array in combination with the sub-band affine projection algorithm (CNMA-SBAPA) is compared with the least mean square (LMS), recursive least square (RLS), traditional APA, distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) in terms of the segmental signal-to-noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ), mean opinion score (MOS), short-time objective intelligibility (STOI), and speed of convergence on real and simulated data for white and colored noises. In all scenarios, the proposed method has high accuracy at different levels and noise types by the lower distortion in comparison with other works and, furthermore, the speed of convergence is higher than the compared researches.
Highlights
In the current century, the smartphones and other communication devices have been an important part of human life, where it is impossible to have social communications without them [1,2]
The proposed system with sub-band affine projection algorithm (APA) is compared by the quantitative, qualitative (PESQ, mean opinion score (MOS), and short-time objective intelligibility (STOI)) criteria, and speed of convergence with the least mean square (LMS), traditional APA, recursive least square (RLS), distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) algorithms on real and simulated data under white and colored noisy conditions
A multi-channel speech enhancement method was proposed based on the microphone array
Summary
The smartphones and other communication devices have been an important part of human life, where it is impossible to have social communications without them [1,2]. A multi-channel speech enhancement method is introduced based on the proposed circular nested microphone array in combination with the sub-band affine projection algorithm (CNMA-SBAPA). The affine projection algorithm (APA), as an adaptive method for the speech enhancement, is implemented on sub-band signals from the circular nested microphone array (NMA). The proposed system with sub-band APA is compared by the quantitative (segmental SNR), qualitative (PESQ, MOS, and STOI) criteria, and speed of convergence with the least mean square (LMS), traditional APA, recursive least square (RLS), distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) algorithms on real and simulated data under white and colored noisy conditions.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
