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Convolutional Recurrent Neural Network Based on Short-Time Discrete Cosine Transform for Monaural Speech Enhancement

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Communications and Networking (ChinaCom 2022)

Abstract

Speech enhancement algorithms based on deep learning have greatly improved speech’s perceptual quality and intelligibility. Complex-valued neural networks, such as deep complex convolution recurrent network (DCCRN), make full use of audio signal phase information and achieve superior performance, but complex-valued operations increase the computational complexity. Inspired by the deep cosine transform convolutional recurrent network (DCTCRN) model, in this paper real-valued discrete cosine transform is used instead of complex-valued Fourier transform. Besides, the ideal cosine mask is employed as the training target, and the real-valued convolutional recurrent network (CRNN) is used to enhance the speech while reducing algorithm complexity. Meanwhile, the frequency-time-LSTM (F-T-LSTM) module is used for better temporal modeling and the convolutional skip connections module is introduced between the encoders and the decoders to integrate the information between features. Moreover, the improved scale-invariant source-to-noise ratio (SI-SNR) is taken as the loss function which enables the model to focus more on the part of signal variation and thus obtain better noise suppression performance. With only 1.31M parameters, the proposed method can achieve noise suppression performance that exceeds DCCRN and DCTCRN.

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Correspondence to Jinzuo Guo .

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Guo, J., Zhou, Y., Liu, H., Ma, Y. (2023). Convolutional Recurrent Neural Network Based on Short-Time Discrete Cosine Transform for Monaural Speech Enhancement. In: Gao, F., Wu, J., Li, Y., Gao, H. (eds) Communications and Networking. ChinaCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 500. Springer, Cham. https://doi.org/10.1007/978-3-031-34790-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-34790-0_13

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  • Online ISBN: 978-3-031-34790-0

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