Conference proceeding
RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, Vol.2020-, pp.4642-4646
International Conference on Acoustics Speech and Signal Processing ICASSP
01/01/2020
DOI: 10.1109/icassp40776.2020.9054090
Abstract
In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported deep learning based approaches work well in localizing a single source directly from multi-channel raw-audio, but are not easily extendable to localize multiple sources due to the well known permutation problem. We propose a novel encoding scheme to represent the spatial coordinates of multiple sources, which facilitates 2D localization of multiple sources in an end-to-end fashion, avoiding the permutation problem and achieving arbitrary spatial resolution. Experiments on a simulated data set and real recordings from the AV16.3 Corpus demonstrate that the proposed method generalizes well to unseen test conditions, and outperforms a recent time difference of arrival (TDOA) based multiple source localization approach reported in the literature.
Details
- Title: Subtitle
- RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES
- Creators
- Harshavardhan Sundar - Amazon Com Inc, Seattle, WA 98109 USAWeiran Wang - AmazonMing Sun - Amazon Com Inc, Seattle, WA 98109 USAChao Wang - Amazon Com Inc, Seattle, WA 98109 USA
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, Vol.2020-, pp.4642-4646
- Publisher
- IEEE
- Series
- International Conference on Acoustics Speech and Signal Processing ICASSP
- DOI
- 10.1109/icassp40776.2020.9054090
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Number of pages
- 5
- Language
- English
- Date published
- 01/01/2020
- Academic Unit
- Computer Science
- Record Identifier
- 9984696719802771
Metrics
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