Abstract
Due to the powerful feature extraction ability, deep learning has become a new trend towards solving speech separation problems. In this paper, we present a novel Deep Neural Network (DNN) architecture for monaural speech separation. Taking into account the good mask property of the human auditory system, a perceptual modified Wiener filtering masking function is applied in the proposed DNN architecture, which is used to make the residual noise perceptually inaudible. The proposed architecture jointly optimize the perceptual modified Wiener filtering mask and DNN. Evaluation experiments on TIMIT database with 20 noise types at different signal-to-noise ratio (SNR) situations demonstrate the superiority of the proposed method over the reference DNN-based separation methods, no matter whether the noise appeared in the training database or not.
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Acknowledgments
This work is supported by NSF of China (Grant No. 61471394, 61402519) and NSF of Jiangsu Province (Grant No. BK20140071, BK20140074).
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Han, W., Zhang, X., Yang, J., Sun, M., Min, G. (2016). Joint Optimization of a Perceptual Modified Wiener Filtering Mask and Deep Neural Networks for Monaural Speech Separation. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9917. Springer, Cham. https://doi.org/10.1007/978-3-319-48896-7_46
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DOI: https://doi.org/10.1007/978-3-319-48896-7_46
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