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http://dx.doi.org/10.7776/ASK.2011.30.8.461

Efficient Implementation of SVM-Based Speech/Music Classification on Embedded Systems  

Lim, Chung-Soo (목포대학교)
Chang, Joon-Hyuk (한양대학교 융합전자공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.30, no.8, 2011 , pp. 461-467 More about this Journal
Abstract
Accurate classification of input signals is the key prerequisite for variable bit-rate coding, which has been introduced in order to effectively utilize limited communication bandwidth. Especially, recent surge of multimedia services elevate the importance of speech/music classification. Among many speech/music classifier, the ones based on support vector machine (SVM) have a strong selling point, high classification accuracy, but their computational complexity and memory requirement hinder their way into actual implementations. Therefore, techniques that reduce the computational complexity and the memory requirement is inevitable, particularly for embedded systems. We first analyze implementation of an SVM-based classifier on embedded systems in terms of execution time and energy consumption, and then propose two techniques that alleviate the implementation requirements: One is a technique that removes support vectors that have insignificant contribution to the final classification, and the other is to skip processing some of input signals by virtue of strong correlations in speech/music frames. These are post-processing techniques that can work with any other optimization techniques applied during the training phase of SVM. With experiments, we validate the proposed algorithms from the perspectives of classification accuracy, execution time, and energy consumption.
Keywords
Support vector machine (SVM); Speech/Music classification algorithm; Embedded system;
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