Dilated Semi-Dense Convolutional Network for Multichannel Speech Enhancement

This paper proposes a dilated semi-dense convolutional network for multichannel speech enhancement using machine learning. The baseline, SpatialNet, employs a Conformer for narrowband processing but uses a simple 3-layer CNN block, limiting local information extraction. To improve performance, we replace the Convolutional Neural Network (CNN) block with dilated convolution, dilated dense convolution, and the proposed dilated semi-dense convolution.

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Publication Details

Journal
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
Published
2026-01-01
DOI
https://doi.org/10.1587/transfun.2025eal2086
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
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article

Dilated Semi-Dense Convolutional Network for Multichannel Speech Enhancement

K. Ichige, Takahiro Murakami, Tomohiro UEYAMA
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
Speech and Audio Processing
article

Dilated Semi-Dense Convolutional Network for Multichannel Speech Enhancement

K. Ichige, Takahiro Murakami, Tomohiro UEYAMA
article en

Abstract

This paper proposes a dilated semi-dense convolutional network for multichannel speech enhancement using machine learning. The baseline, SpatialNet, employs a Conformer for narrowband processing but uses a simple 3-layer CNN block, limiting local information extraction. To improve performance, we replace the Convolutional Neural Network (CNN) block with dilated convolution, dilated dense convolution, and the proposed dilated semi-dense convolution.

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences
Meiji University (JP), Yokohama National University (JP)
Openalex Percentile: Top 96%
Speech and Audio Processing
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