Publication Details
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation and Extraction
Long Yanhua
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
DPCCN, Mixture-Remix, cross-domain, speech separation, unsupervised target speech extraction
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.
@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"
}