Low-complexity acoustic scene classification using data generation based on primary ambient extraction
Low-complexity acoustic scene classification using data generation based on primary ambient extraction
Acoustic scene classification (ASC) is an important branch of machine hearing. Since ASC systems are intended to be deployed on mobile devices, how to ensure the performance under low-complexity implementation has become an attracting research problem. The state-of-the-art methods include compressing parameter precisions, reducing quantization bits, introducing sparsity constraints and so on. These methods mainly focus on the model level optimization, while explorations are rarely originated from the data level. This paper introduces a train of thoughts from data level, inspired by a stereo audio processing algorithm, namely the primary ambient extraction (PAE), which generates additional samples through audio up-mixing. The experiment results demonstrate that the proposed method exhibits better performance than a group of ASC baseline systems without data level optimization, not to mention that the proposed method is compatible with the existing model level optimization.
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Yang, Haocong
b222b76e-59c5-40cc-9e0c-52fea538f7ee
Liu, Yingzi
2f329d69-3fc5-4271-9426-f623cc28f76c
Liang, Jiangnan
a42f52fd-6d3f-4466-85a4-9a97f17aba4d
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Yang, Haocong
b222b76e-59c5-40cc-9e0c-52fea538f7ee
Liu, Yingzi
2f329d69-3fc5-4271-9426-f623cc28f76c
Liang, Jiangnan
a42f52fd-6d3f-4466-85a4-9a97f17aba4d
Shi, Chuang, Yang, Haocong, Liu, Yingzi and Liang, Jiangnan
(2021)
Low-complexity acoustic scene classification using data generation based on primary ambient extraction.
In 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).
IEEE.
5 pp
.
(doi:10.1109/BMSB53066.2021.9547178).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Acoustic scene classification (ASC) is an important branch of machine hearing. Since ASC systems are intended to be deployed on mobile devices, how to ensure the performance under low-complexity implementation has become an attracting research problem. The state-of-the-art methods include compressing parameter precisions, reducing quantization bits, introducing sparsity constraints and so on. These methods mainly focus on the model level optimization, while explorations are rarely originated from the data level. This paper introduces a train of thoughts from data level, inspired by a stereo audio processing algorithm, namely the primary ambient extraction (PAE), which generates additional samples through audio up-mixing. The experiment results demonstrate that the proposed method exhibits better performance than a group of ASC baseline systems without data level optimization, not to mention that the proposed method is compatible with the existing model level optimization.
Text
BMSB2021_Yang_Submission
- Accepted Manuscript
Available under License Other.
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e-pub ahead of print date: 1 October 2021
Venue - Dates:
2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), , Chengdu, China, 2021-08-04 - 2021-08-06
Identifiers
Local EPrints ID: 484138
URI: http://eprints.soton.ac.uk/id/eprint/484138
PURE UUID: 4aa0c22e-0332-4223-b596-4e596014a98d
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Date deposited: 10 Nov 2023 18:03
Last modified: 18 Mar 2024 04:13
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Contributors
Author:
Chuang Shi
Author:
Haocong Yang
Author:
Yingzi Liu
Author:
Jiangnan Liang
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