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Change detection within an audio stream is an important task in severaldomains, such as classification and segmentation of a sound or of a musicpiece, as well as indexing of broadcast news or surveillance applications. Inthis paper we propose two novel methods for spectral change detection withoutany assumption about the input sound: they are both based on the evaluation ofinformation measures applied to a time- frequency representation of the signal,and in particular to the spectrogram. The class of measures we consider, theR\'enyi entropies, are obtained by extending the Shannon entropy definition: abiasing of the spectrogram coefficients is realized through the dependence ofsuch measures on a parameter, which allows refined results compared to thoseobtained with standard divergences. These methods provide a low computationalcost and are well-suited as a support for higher level analysis, segmentationand classification algorithms.

R\'enyi Information Measures for Spectral Change Detection