Abstract
In this paper, a new method based on the Electroglottography (EGG) for solving speech separation problem was proposed. By using the EGG, the mixed speech signals was segmented to silence segment S, unvoiced segment U, and voiced segment V (SUV segmentation) according to the feature of the voiced and unvoiced speech. The V-segment algorithm was based on Blind Source Separation (BSS) using FastICA. Computational Auditory Scene Analysis (CASA) and spectral subtraction methods were used to obtain the target unvoiced Ideal Binary Mask (IBM). And the waveform synthesis was then performed to obtain the target unvoiced sound for U-segment. The experimental results showed that the proposed algorithm can improve SNR, similarity coefficient and subjective evaluation with EGG.
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Acknowledgements
This work was supported by the Fundamental Research Funds for the Central Universities (Project number: YWF-19-BJ-J-197) and the National Natural Science Foundation (Project number: 61603013). The author would like to thank the anonymous reviewers for their valuable suggestions and remarks.
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Chen, L., Sun, L., Mao, X. (2020). Separation of Speech from Speech Interference Based on EGG. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_5
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DOI: https://doi.org/10.1007/978-3-030-19738-4_5
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