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Single-Channel Dereverberation and Denoising Based on Lower Band Trained SA-LSTMs

Lookup NU author(s): Yi Li, Dr Mohsen Naqvi

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This is the authors' accepted manuscript of an article that has been accepted and is due to be published in its final definitive form by Institution of Engineering and Technology (IET) , 2021.

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Abstract

The supervised single-channel speech enhancement presents one mixture recording at the input of neural network and updates network parameters in order to generate an output as the reconstructed speech signal. However, current neural networks based single-channel speech enhancement methods are not able to fully utilize pertinence with the specific frequency range of speech signals with limited computational complexity. In this paper, we study the power spectral density (PSD) of mixtures with human speech and noise interferences. Based on the theory that the speech signal distributes at the lower band, we propose a method to train signal approximation (SA) based neural networks with the lower frequency band of the speech mixture to improve the performance. To realize the lower band approach for single-channel speech enhancement, the method uses a long short-term memory (LSTM) block to exploit short-time Fourier transform (STFT) of the desired frequency range. Furthermore, in order to improve the speech enhancement performance within reverberant room environments, the dereverberation mask (DM) and the enhanced ratio mask (ERM) are exploited as the training targets of two LSTM blocks, respectively. The detailed evaluations confirm that the proposed method outperforms the state-of-the-art methods.


Publication metadata

Author(s): Li Y, Sun Y, Naqvi SM

Publication type: Article

Publication status: In Press

Journal: IET Signal Processing

Year: 2021

Acceptance date: 08/12/2020

Date deposited: 15/12/2020

ISSN (print): 1751-9675

ISSN (electronic): 1751-9683

Publisher: Institution of Engineering and Technology (IET)


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