Abstract
In this work, we present a two-stage method for speaker extraction under reverberant and noisy conditions. Given a reference signal of the desired speaker, the clean, but the still reverberant desired speaker is first extracted from the noisy-mixed sign'al. In the second stage, the extracted signal is further enhanced by joint dereverberation and residual noise and interference reduction. The proposed architecture comprises two sub-networks, one for the extraction task and the second for the dereverberation task. We present a training strategy for this architecture and show that the performance of the proposed method is on par with other state-of-the-art (SOTA) methods when applied to the WHAMR! dataset. Furthermore, we present a new dataset with more realistic adverse acoustic conditions and show that our method outperforms the competing methods when applied to this dataset as well.
Original language | English |
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Title of host publication | 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings |
Publisher | European Signal Processing Conference, EUSIPCO |
Pages | 266-270 |
Number of pages | 5 |
ISBN (Electronic) | 9789464593600 |
DOIs | |
State | Published - 2023 |
Event | 31st European Signal Processing Conference, EUSIPCO 2023 - Helsinki, Finland Duration: 4 Sep 2023 → 8 Sep 2023 |
Publication series
Name | European Signal Processing Conference |
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ISSN (Print) | 2219-5491 |
Conference
Conference | 31st European Signal Processing Conference, EUSIPCO 2023 |
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Country/Territory | Finland |
City | Helsinki |
Period | 4/09/23 → 8/09/23 |
Bibliographical note
Publisher Copyright:© 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
Funding
This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 871245.
Funders | Funder number |
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Horizon 2020 | 871245 |
Keywords
- Dereverberation
- Speaker extraction