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In this paper, an improved music genre classification method is presented. The proposed method makes use of the wavelet package transform (WPT) and the best basis algorithm (BBA) to accurately classify and increase classification performance. It is well known that WPT can generate a wavelet decomposition that offers a richer signal analysis. In this paper, the music signal is first decomposed into...
This paper proposes a new music genre classification algorithm based on dynamic music frame analysis and support vector machine (SVM). The dynamic music frame analysis could cover the long-term and the short-term music genre features which can represent the time-varying behavior of music signals. The music genre features used in this paper are mel-frequency cepstral coefficient (MFCC) and log energy...
This paper proposed a new voice activity detection (VAD) algorithm using support vector machine (SVM) for improving the VAD performance of AMR-WB speech codec. The SVM is applied to train an optimized non-linear decision rule involving the VAD parameters, e.g., sub-band signal level, pitch gain, background noise level, and etc., defined in AMR-WB standard. Then, by the use of the trained SVM, the...
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