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    %0 Conference Proceedings
    %A Rossignol, Stéphane
    %A Rodet, Xavier
    %A Soumagne, Joel
    %A Colette, Jean-Louis
    %A Depalle, Philippe
    %T Feature extraction and temporal segmentation of acoustic signals
    %D 1998
    %B ICMC: International Computer Music Conference
    %C Ann Arbor
    %F Rossignol98a
    %K segmentation
    %K signal representation
    %K feature extraction
    %K coding
    %K multimedia
    %X This paper deals with temporal segmentation of acoustic signals and feature extraction. Segmentation and feature extraction are aimed at being a first step for sound signal representation, coding, transformation and multimedia. Three interdependent levels of segmentation are defined. They correspond to different levels of signal attributes. The Source level distinguishes speech, singing voice, instrumental parts and other sounds, such as street sounds, machine noise... The Feature level deals with characteristics such as silence/sound, transitory/steady, voiced/unvoiced, harmonic, vibrato and so forth. The last level is the segmentation into Notes and Phones. A large set of features is first computed: derivative and relative derivative of f0 and energy, voicing coefficient, mesure of the inharmonicity of the partials, spectral centroid, spectral ``flux'', high order statistics, energy modulation, etc. A decision function on the set of features has been built and provides the segmentation marks. It also depends on the current application and the required result. As an example, in the case of the singing voice, segmentation according to pitch is different from segmentation into phones. A graphical interface allows visualization of these features, the results of the decisions, and the final result. For the Source level, some features are predominant: spectral centroid, spectral flux, energy modulation and their variance computed on a sound segment of one second or more. Segmentation starts with the Source level, but the three levels are not independent. Therefore, information obtained at a given level is propagated towards the other levels. For example, in case of instrumental music and the singing voice, if vibrato is detected at the Feature level, amplitude and frequency of the vibrato are estimated and are taken into account for the Notes and Phones level. The vibrato is removed from the f0 trajectory, and the high frequencies of the signal are not used in spectral flux computation. A complete segmentation and feature extraction system is demonstrated. Applications and results on various examples such as a movie sound track are presented.
    %1 7
    %2 3

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