Title :
Acoustic Scene Analysis Based on Power Decomposition
Author :
de Cheveigne, Alain ; Slama, Michael
Author_Institution :
CNRS, Paris Univ.
Abstract :
A method is proposed for the analysis of acoustic scenes. The contribution of each competing source is suppressed on the basis of harmonic structure or cross-sensor correlation, in such a way that other sources may be estimated. Successive suppression of sources allows the scene to be characterized. In the limit of purely periodic sources and no noise, the method provides accurate estimates of fundamental frequency and spectral content. In the presence of noise or imperfect periodicity, it offers likelihood functions from which the spectral content of sources may be inferred using Bayesian methods. The method is an alternative to more familiar spectral and spectro-temporal methods of acoustic scene analysis. An advantage is that precise analysis is possible using short temporal windows
Keywords :
Bayes methods; acoustic signal processing; spectral analysis; Bayesian methods; acoustic scene analysis; cross-sensor correlation; harmonic structure; imperfect periodicity; likelihood functions; power decomposition; purely periodic sources; spectral methods; spectro-temporal methods; successive source suppression; Acoustic noise; Autocorrelation; Bayesian methods; Frequency estimation; Image analysis; Layout; Power harmonic filters; Shape; Smoothing methods; Speech;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1661209