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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144910" target="_blank" >RIV/00216305:26230/22:PU144910 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9747340" target="_blank" >https://ieeexplore.ieee.org/document/9747340</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747340" target="_blank" >10.1109/ICASSP43922.2022.9747340</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISBN

978-1-6654-0540-9

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

7292-7296

Publisher name

IEEE Signal Processing Society

Place of publication

Singapore

Event location

Singapore

Event date

May 22, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000864187907119