保留立体声相位信息的声音场景分类系统
保留立体声相位信息的声音场景分类系统
With increasing devices supporting the recording of binaural audios, binaural audio processing methods become a field of possible exploration in acoustic scene classification (ASC). Therefore, we would like to investigate the primary ambient extraction (PAE), a binaural audio processing method which decomposes a binaural audio sample into four channels using the phase information. Features carrying binaural phase information were therefore extracted. An ensemble of convolution neural networks (CNNs) was adopted as the classifier. Compared to existing works, the ASC system proposed in this paper can generate features with additional phase information and make full use of the advantages of binaural audios. The evaluation results validate that the performance of our ASC system can be improved by taking the binaural phase information into account. Our ASC system outperforms the baseline system provide by the 2019 IEEE AASP Challenge Detection and Classification of Acoustic Scenes and Events (DCASE） by 18.3% in terms of the classification accuracy.
871-878
Yang, Haocong
47ef7142-5739-4d1d-8a21-d736906a12c6
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Li, Huiyong
01099860-a8cb-4a57-b2b3-f5a426fcba2c
Yang, Haocong
47ef7142-5739-4d1d-8a21-d736906a12c6
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Li, Huiyong
01099860-a8cb-4a57-b2b3-f5a426fcba2c
Abstract
With increasing devices supporting the recording of binaural audios, binaural audio processing methods become a field of possible exploration in acoustic scene classification (ASC). Therefore, we would like to investigate the primary ambient extraction (PAE), a binaural audio processing method which decomposes a binaural audio sample into four channels using the phase information. Features carrying binaural phase information were therefore extracted. An ensemble of convolution neural networks (CNNs) was adopted as the classifier. Compared to existing works, the ASC system proposed in this paper can generate features with additional phase information and make full use of the advantages of binaural audios. The evaluation results validate that the performance of our ASC system can be improved by taking the binaural phase information into account. Our ASC system outperforms the baseline system provide by the 2019 IEEE AASP Challenge Detection and Classification of Acoustic Scenes and Events (DCASE） by 18.3% in terms of the classification accuracy.
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保留立体声相位信息的声音场景分类系统
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e-pub ahead of print date: 25 June 2020
Alternative titles:
Acoustic scene classification system using binaural phase information
Identifiers
Local EPrints ID: 484468
URI: http://eprints.soton.ac.uk/id/eprint/484468
ISSN: 1003-0530
PURE UUID: a3a9aa75-5479-424d-a5a5-1c8deb9443a5
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Date deposited: 16 Nov 2023 13:12
Last modified: 18 Mar 2024 04:13
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Author:
Haocong Yang
Author:
Chuang Shi
Author:
Huiyong Li
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