Acoustic Scene Classification on Large Dataset Using Sparse Auto-encoder Based Deep Neural Network

DOI

10.2991/itim-17.2017.7How to use a DOI?

Keywords

Acoustic scene classification, auto-encoder, deep neural network, big data

Abstract

In this paper we study the acoustic scene classification using a large dataset. The spectrogram of the large acoustic samples are extracted and applied with texture feature classification method. First, the acoustic scene database is built including various acoustic events. Second the image texture features on spectrogram are used to represent the acoustic samples. Third the auto-encoder is adopted to build a deep neural network classifier. Finally, we verified the proposed system on a large number of dataset and compared our results with traditional Gaussian mixture model and three-layer neural network. The experimental results show that the proposed method is effective and promising in big acoustic data classification.

Copyright

© 2017, 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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TY  - CONF
AU  - Jianqiang Tan
PY  - 2017/08
DA  - 2017/08
TI  - Acoustic Scene Classification on Large Dataset Using Sparse Auto-encoder Based Deep Neural Network
BT  - Proceedings of the 2017 International Conference on Information Technology and Intelligent Manufacturing (ITIM 2017)
PB  - Atlantis Press
SP  - 27
EP  - 30
SN  - 1951-6851
UR  - https://doi.org/10.2991/itim-17.2017.7
DO  - 10.2991/itim-17.2017.7
ID  - Tan2017/08
ER  -