1.Algorithm of Abnormal Audio Recognition Based on Improved MFCC 2012.pdf
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Procedia Engineering 29 (2012) 731 – 737
1877-7058 ? 2011 Published by Elsevier Ltd.
doi:10.1016/eng.2012.01.032
Available online at
Available online at
Procedia Engineering 00 (2012) 000–000
Procedia
Engineering
/locate/procedia
2012 International Workshop on Information and Electronics Engineering (IWIEE)
Algorithm of Abnormal Audio Recognition Based on
Improved MFCC
Chuan Xiea,Xiaoli Caoa,Lingling Hea
aCollege of computer science and information engineering, Chongqing technology and business university, Chongqing 400067,
china
Abstract
Characteristics extraction has a great effect on the audio training and recognition in the audio recognition system.
MFCC algorithm is a typical characteristics extraction method with stable performance and high recognition rate. For
the situation that MFCC has a large amount of computation, an improved algorithm MFCC_E is introduced. The
computation of MFCC_E is reduced by 50% compared with the standard algorithm MFCC, and it make the hardware
implementation is easy. The experimental result indicated that MFCC_E and MFCC have the same recognition rate
roughly, yet the computational complexity of MFCC_E is much smaller.
? 2012 Published by Elsevier Ltd.
Keywords:audio recognition; characteristics extraction; MFCC; MFCC_E; GMM
1. Introduction
In the field of target tracking, the video and the radio data are the two most important kinds of
information, and in the past two decades, the video tracking has been in a dominant position, but its
tracking performance will be greatly reduced when the tracking target is out of the observation range.
Compared with video tracking, acoustic sensor has the advantages of low cost, small size, high efficiency,
and the audio signal changes slowly over time, so the collected audio signal is stable and reliable.
Therefore, the audio recognition and the audio target localization have become the research hotspots in
recent years [1] .
In the field of au
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