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DOI: 10.14569/IJACSA.2022.0131088
PDF

Multi-Channel Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional Network

Author 1: Nguyen Huu Binh
Author 2: Duong Van Hai
Author 3: Bui Tien Dat
Author 4: Hoang Ngoc Chau
Author 5: Nguyen Quoc Cuong

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: The Minimum Variance Distortionless Response (MVDR) beamforming algorithm is frequently utilized to extract speech and noise from noisy signals captured from multiple microphones. A frequency-time mask should be employed to compute the Power Spectral Density (PSD) matrices of the noise and the speech signal of interest to obtain the optimal weights for the beamformer. Deep Neural Networks (DNNs) are widely used for estimating time-frequency masks. This paper adopts a novel method using Graph Convolutional Networks (GCNs) to learn spatial correlations among the different channels. GCNs are integrated into the embedding space of a U-Net architecture to estimate a Complex Ideal Ratio Mask (cIRM). We use the cIRM in an MVDR beamformer to further improve the enhancement system. We simulate room acoustics data to experiment extensively with our approach using different types of the microphone array. Results indicate the superiority of our approach when compared to current state-of-the-art methods. The metrics obtained by the proposed method are significantly improved, except the Scale-Invariant Source-to-Distortion Ratio (SI-SDR) score. The Perceptual Evaluation of Speech Quality (PESQ) score shows a noticeable improvement over the baseline models (i.e., 2.207 vs. 2.104 and 2.076). Our implementation of the proposed method can be found in the following link: https://github.com/3i-hust-asr/gnn-mvdr-final.

Keywords: Multi-Channel Speech Enhancement; Graph Convolutional Networks; Minimum Variance Distortionless Response Beamformer; Complex Ideal Ratio Mask

Nguyen Huu Binh, Duong Van Hai, Bui Tien Dat, Hoang Ngoc Chau and Nguyen Quoc Cuong, “Multi-Channel Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131088

@article{Binh2022,
title = {Multi-Channel Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131088},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131088},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Nguyen Huu Binh and Duong Van Hai and Bui Tien Dat and Hoang Ngoc Chau and Nguyen Quoc Cuong}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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