RÉNYI INFORMATION MEASURES FOR SPECTRAL CHANGE DETECTION

Marco Liuni (1, 2) , Axel Röbel (1) , Marco Romito (2) , Xavier Rodet (1)

1 IRCAM - Institut de Recherche et Coordination Acoustique/Musique
2 Dipartimento di Matematica "Ulisse Dini"

Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time- frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the Rényi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Marco Liuni, Axel Röbel, Marco Romito, Xavier Rodet. RÉNYI INFORMATION MEASURES FOR SPECTRAL CHANGE DETECTION. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. pp.3824-3827. ⟨hal-00627002⟩