Publication Details

DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation and Extraction

HAN, J.; LONG, Y.; BURGET, L.; ČERNOCKÝ, J. DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation and Extraction. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Singapore: IEEE Signal Processing Society, 2022. p. 7292-7296. ISBN: 978-1-6654-0540-9.
Czech title
DPCCN: Hustě propojená pyramidální komplexní konvoluční síť pro robustní separaci a extrakci řeči
Type
conference paper
Language
English
Authors
Han Jiangyu (DCGM)
Long Yanhua
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
URL
Keywords

DPCCN, Mixture-Remix, cross-domain, speech separation, unsupervised target speech
extraction

Abstract

In recent years, a number of time-domain speech separation methods have been
proposed. However, most of them are very sensitive to the environments and wide
domain coverage tasks. In this paper, from the time-frequency domain perspective,
we propose a densely-connected pyramid complex convolutional network, termed
DPCCN, to improve the robustness of speech separation under complicated
conditions. Furthermore, we generalize the DPCCN to target speech extraction
(TSE) by integrating a new specially designed speaker encoder. Moreover, we also
investigate the robustness of DPCCN to unsupervised cross-domain TSE tasks.
A Mixture-Remix approach is proposed to adapt the target domain acoustic
characteristics for fine-tuning the source model. We evaluate the proposed
methods not only under noisy and reverberant in-domain condition, but also in
clean but cross-domain conditions. Results show that for both speech separation
and extraction, the DPCCN-based systems achieve significantly better performance
and robustness than the currently dominating time-domain methods, especially for
the crossdomain tasks. Particularly, we find that the Mixture-Remix finetuning
with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised
cross-domain TSE, with around 3.5 dB SISNR improvement on target domain test set,
without any source domain performance degradation.

Published
2022
Pages
7292–7296
Proceedings
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), Singapore, SG
ISBN
978-1-6654-0540-9
Publisher
IEEE Signal Processing Society
Place
Singapore
DOI
10.1109/ICASSP43922.2022.9747340
UT WoS
000864187907119
EID Scopus
2-s2.0-85129574325
BibTeX 
@inproceedings{BUT178382,
  author="Jiangyu {Han} and Yanhua {Long} and Lukáš {Burget} and Jan {Černocký}",
  title="DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation and Extraction",
  booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
  year="2022",
  pages="7292--7296",
  publisher="IEEE Signal Processing Society",
  address="Singapore",
  doi="10.1109/ICASSP43922.2022.9747340",
  isbn="978-1-6654-0540-9",
  url="https://ieeexplore.ieee.org/document/9747340"
}
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