The Classification of Environmental Audio with Ensemble Learning

DOI

10.2991/icacsei.2013.93How to use a DOI?

Keywords

Ensemble learning, Environmental Audio, Bagging, Boosting.

Abstract

Environmental audio classification has been the focus in the field of speech recognition. The paper proposes using ensemble learning to perform environmental audio classification. Ensemble learning is to train multiple component learners and combine their predictions to enhance the accuracy of classification. The paper explains the ensemble algorithms and strategies, and the comparison and analysis of classification results are given which obtain by employing the single classification algorithm, Bagging, Boosting, Random Forest and MCS ensemble classifiers for the given environmental audio data. The experimental results show that the ensemble technology can effectively improve the performance of environmental audio data classification. Even under the fewer number of the training examples, it provides a kind of effective approach to guarantee the performance and generalization of classification.

Copyright

© 2013, 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/).

Download article (PDF)

TY  - CONF
AU  - Yan Zhang
AU  - Dan jv Lv
AU  - Ying Lin
PY  - 2013/08
DA  - 2013/08
TI  - The Classification of Environmental Audio with Ensemble Learning
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 368
EP  - 371
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.93
DO  - 10.2991/icacsei.2013.93
ID  - Zhang2013/08
ER  -