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.
Authors
- K. Ichige
- Takahiro Murakami (ORCID: https://orcid.org/0000-0003-0615-9939)
- Tomohiro UEYAMA
Institutions
- Meiji University (JP)
- Yokohama National University (JP)
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