Detection of abrupt spectral changes using support vector machines an application to audio signal segmentation

Abstract

In this paper, we introduce an hybrid time-frequency/support vector machine algorithm for the detection of abrupt spectral changes. A stationarity index is derived from support vector novelty detection theory by using sub-images extracted from the time-frequency plane as feature vectors. Simulations show the efficiency of this new algorithm for audio signal segmentation, compared to another nonparametric detector.