Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

Environmental Audio Classification Based on Active Learning with SVM

Authors
Yan Zhang, Danjv Lv, Ying Lin
Corresponding Author
Yan Zhang
Available Online July 2015.
DOI
https://doi.org/10.2991/csic-15.2015.50How to use a DOI?
Keywords
Active learning, Environmental audio classification, Support vector machines, SVM_EPS, MOA
Abstract

In order to solve the classification of environmental audio data under the fewer number of the training examples, this paper combined Support Vector Machines SVM) and Entropy Priority Sampling (EPS), and proposed the SVM_EPS method as the selecting sampling strategies in active learning. And the method MOA (Multi-variant Optimization Algorithm) was exploited to select the optimal kernel parameters of SVM. In experiments, the CELP features in 11 dimensions were extracted from the given environmental audio data, and the classification performances were compared under different percent training samples with SVM, EPS and SVM_EPS. The results show that SVM_EPS method outperforms the SVM and EPS.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
Series
Advances in Computer Science Research
Publication Date
July 2015
ISBN
978-94-62520-84-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/csic-15.2015.50How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yan Zhang
AU  - Danjv Lv
AU  - Ying Lin
PY  - 2015/07
DA  - 2015/07
TI  - Environmental Audio Classification Based on Active Learning with SVM
BT  - Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
PB  - Atlantis Press
SP  - 208
EP  - 212
SN  - 2352-538X
UR  - https://doi.org/10.2991/csic-15.2015.50
DO  - https://doi.org/10.2991/csic-15.2015.50
ID  - Zhang2015/07
ER  -