IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532

This article has now been updated. Please use the final version.

CNN-based feature integration network for speech enhancement in microphone arrays
Ji XIPengxu JIANGYue XIEWei JIANGHao DING
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JOURNAL FREE ACCESS Advance online publication

Article ID: 2024EDL8014

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Abstract

The relevant model based on convolutional neural networks (CNNs) has been proven to be an effective solution in speech enhancement algorithms. However, there needs to be more research on CNNs based on microphone arrays, especially in exploring the correlation between networks associated with different microphones. In this paper, we proposed a CNN-based feature integration network for speech enhancement in microphone arrays. The input of CNN is composed of short-time Fourier transform (STFT) from different microphones. CNN includes the encoding layer, decoding layer, and skip structure. In addition, the designed feature integration layer enables information exchange between different microphones, and the designed feature fusion layer integrates additional information. The experiment proved the superiority of the designed structure.

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