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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 650818, 6 pages
http://dx.doi.org/10.1155/2012/650818
Research Article

Environmental Sound Recognition Using Time-Frequency Intersection Patterns

1Graduate Department of Computer and Information Systems, Graduate School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu 965-8580, Japan
2Department of Computer Science and Engineering, Shanghai Jiaotong University, 200240 Shanghai, China

Received 13 January 2012; Accepted 27 February 2012

Academic Editor: Zhishun She

Copyright © 2012 Xuan Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations.