A new SVM-based mix audio classification

A preprocessing stage in every speech/music applications including separation, recognition and transcription task is inevitable to determine each frame belongs to which classes, namely: speech only, music only or speech/music mixture. Such classification can significantly decrease the computational burden due to exhaustive search commonly introduced as a problem in model-based speech recognition or separation as wen as music transcription scenarios. In this paper, we present a new method to separate mixed type audio frames based on Support Vector Machine (SVM). The challenging problem in this work is seeking the most appropriate features to discriminate these classes. As a result, we propose some novel features based on eigen-decomposition which presents acceptable classification result. The experimental results show that the proposed system outperforms other classification systems including k Nearest Neighbor (k-NN), Multi-Layer Perceptron (MLP).