A Causal Rhythm Grouping
Identifieur interne : 001C08 ( Main/Exploration ); précédent : 001C07; suivant : 001C09A Causal Rhythm Grouping
Auteurs : Kristoffer Jensen [Danemark]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: This paper presents a method to identify segment boundaries in music. The method is based on a hierarchical model; first a features is measured from the audio, then a measure of rhythm is calculated from the feature (the rhythmogram), the diagonal of a self-similarity matrix is calculated from the rhythmogram, and finally the segment boundaries are found on a smoothed novelty measure, calculated from the diagonal of the self-similarity matrix. All the steps of the model have been accompanied with an informal evaluation, and the final system is tested on a variety of rhythmic songs with good results. The paper introduces a new feature that is shown to work significantly better than previously used features, a robust rhythm model and a robust, relatively cheap method to identify structure from the novelty measure.
Url:
DOI: 10.1007/978-3-540-31807-1_6
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: This paper presents a method to identify segment boundaries in music. The method is based on a hierarchical model; first a features is measured from the audio, then a measure of rhythm is calculated from the feature (the rhythmogram), the diagonal of a self-similarity matrix is calculated from the rhythmogram, and finally the segment boundaries are found on a smoothed novelty measure, calculated from the diagonal of the self-similarity matrix. All the steps of the model have been accompanied with an informal evaluation, and the final system is tested on a variety of rhythmic songs with good results. The paper introduces a new feature that is shown to work significantly better than previously used features, a robust rhythm model and a robust, relatively cheap method to identify structure from the novelty measure.</div>
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