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Underdetermined blind speech source separation based on deep nearest neighbor clustering algorithm

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Abstract

In this paper, we propose a new autoencoder network architecture with clustering mechanism for underdetermined blind speech source separation, i.e., the number of mixtures is less than that of sources. The autoencoder network is employed to project the mixtures to embedding space and obtain their embedding vectors. The network model additionally incorporates the clustering mechanism and nearest neighbor clustering algorithm to estimate the clustering centers of the embedding vectors. Then, according to the embedding vectors, the hard and the probability assignment method are proposed to assign the mixtures to their corresponding clusters to recover the sources. The experimental results demonstrate that the proposed method yields better performance compared to the baseline algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61866024).

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  1. Department of Electronic Information Engineering, Nanchang University, Nanchang, China

    Mengdie Niu & Ye Zhang

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  1. Mengdie Niu
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  2. Ye Zhang
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Correspondence to Ye Zhang.

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This work was supported by the National Natural Science Foundation of China (Grant No. 61866024).

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Niu, M., Zhang, Y. Underdetermined blind speech source separation based on deep nearest neighbor clustering algorithm. Multimed Tools Appl 82, 1171–1183 (2023). https://doi.org/10.1007/s11042-022-13009-5

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