Abstract
Speech separation plays an important role in a speech-related system since it can denoise, extract, and enhance speech signals. In recent years, many methods are proposed to separate the human voice of noise and other sounds. To separate the speech from a complicated signal, we propose a more powerful method by using a VAE model and then post-processing with a bandpass filter. This combination can use to extract the original human speech in the mixture with not only high-frequency noise but also many different sounds. Our approach can be flexibly applied for the new background sounds.
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Do, H.D., Tran, S.T., Chau, D.T. (2020). A Variational Autoencoder Approach for Speech Signal Separation. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_43
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