Hydranet: A Real-Time Waveform Separation Network

Esbern Torgard Kaspersen, Tsampikos Kounalakis, Cumhur Erkut

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

2 Citationer (Scopus)

Abstrakt

Real-time source separation has become increasingly important, as more and more applications, such as voice recognition and voice commands, require clean audio input in noisy environments. Recent developments in deep learning have allowed models to directly exploit the waveform of the audio, making real-time separation achievable. In this paper, we propose a 1-D convolutional U-Net structure to separate waveform input. This structure incorporates recurrent layers, to exploit longer temporal connections in the audio signal. Our proposed network architecture is also benefiting from the addition of an extra output channel, measuring the distortion of the other output channels. Our proposed methodology is experimentally shown to yield state-of-the-art results, using only 0.76 seconds of input audio.
OriginalsprogEngelsk
Titel2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Antal sider5
ForlagIEEE
Publikationsdatomaj 2020
Sider4327-4331
Artikelnummer9053357
ISBN (Elektronisk)978-1-5090-6631-5
DOI
StatusUdgivet - maj 2020
Begivenhed2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spanien
Varighed: 4 maj 20208 maj 2020

Konference

Konference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Land/OmrådeSpanien
ByBarcelona
Periode04/05/202008/05/2020
SponsorThe Institute of Electrical and Electronics Engineers, Signal Processing Society
NavnICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Vol/bind2020-May
ISSN1520-6149

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