Efficient implementation techniques of an SVM-based speech/music classifier in SMV

For real-time speech and audio encoders used in various multimedia applications, low-complexity encoding algorithms are required. Indeed, accurate classification of input signals is the key prerequisite for variable bit rate encoding, which has been introduced in order to effectively utilize limited communication bandwidth. This paper investigates implementation issues with a support vector machine (SVM)-based speech/music classifier in the selectable mode vocoder (SMV) framework, which is a standard codec adopted by the Third-Generation Partnership Project 2 (3GPP2). While a support vector machine is well known for its superior classification capability, it is accompanied by a high computational cost. In order to achieve a more realizable system, we propose two techniques for the SVM-based speech/music classifier, aimed at reducing the number of classification requests to the classifier. The first technique introduces a simpler classifier that processes some of the input frames instead of the SVM-based classifier, and the second technique skips a portion of input frames based on strong inter-frame correlation in speech and music frames. Our experimental results show that the proposed techniques can reduce the computational cost of the SVM-based classifier by 95.4 % with negligible performance degradation, making it plausible for integration into the SMV codec.