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%0 Conference Proceedings
%A Rodet, Xavier
%A Rossignol, Stéphane
%T Automatic characterisation of musical signals : feature extraction and temporal segmentation
%D 1998
%B ACM MULTIMEDIA 98
%C Bristol
%F Rodet98b
%K characterisation
%K segmentation
%K musical signal
%K feature extraction
%X This paper presents some results on automatic characterisation of musical and acoustic signals in terms of features attributed to signal segments at various levels. These features describe some of the musical and acoustical content of the sound and can be used in applications such as intelligent sound processing, retrieval of music and sound databases or music editing and labelling. Three interdependent levels of segmentation are defined. They correspond to different levels of signal attributes. The {\it source} level classifies the nature of the source of sound into speech, singing voice, instrumental sounds and various noises. The {\it 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 {\it notes} and {\it phones}. A large set of features is first computed: derivative of fundamental frequency and energy, voicing coefficient, measure of the inharmonicity of the partials, spectral centroid, spectral ``flux'', etc. Decision functions on the set of features have been built and provide the segmentation marks. For research purposes, a graphical interface has been designed to allow visualization of these features, the results of the decisions, and the final result. For the {\it source} level the mean and the variance of the features are computed on sound segments of one second or more. Various classification methods are used which are trained with data sets collected by sampling radio broadcasts and movie sound tracks. Segmentation starts with the {\it source} level. 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 {\it feature} level, amplitude and frequency of vibrato are estimated and are taken into account for the {\it notes} and {\it phones} level. The vibrato is removed from the fundamental frequency trajectory, and the high frequencies of the signal are not used in spectral flux computation. A complete feature extraction and segmentation system is demonstrated. Applications and results on various examples such as a movie sound track are presented.
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