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Frame-Level Speech/Music Discrimination using AdaBoost

In this paper we adapt an AdaBoost-based image processing algorithm to the task of predicting whether an audio signal contains speech or music. We derive a frame-level discriminator that is both fast and accurate. Using a simple FFT and no built-in prior knowledge of signal structure we obtain an accuracy of 88% on frames sampled at 20ms intervals. When we smooth the output of the classifier with the output of the previous 40 frames our forecast rate rises to 93% on the Scheirer-Slaney (Scheirer and Slaney, 1997) database. To demonstrate the efficiency and effectiveness of the model, we have implemented it as a graphical real-time plugin to the popular Winamp audio player.