InforLorV4, Main, Exploration, bibRecord, 000F69

Melody harmonisation with interpolated probabilistic models

Identifieur interne : 000F69 ( Main/Exploration ); précédent : 000F68; suivant : 000F70

Melody harmonisation with interpolated probabilistic models

Auteurs : Stanislaw Raczynski [France] ; Satoru Fukayama [Japon] ; Emmanuel Vincent [France]

Source :

Journal of New Music Research [ 0929-8215 ] ; 2013-10-22.

RBID : Hal:hal-00876128

Abstract

Most melody harmonisation systems use the generative hidden Markov model (HMM), which model the relation between the hidden chords and the observed melody. Relations to other variables, such as the tonality or the metric struc- ture, are handled by training multiple HMMs or are ignored. In this paper, we propose a discriminative means of combining multiple probabilistic models of various musical variables by means of model interpolation. We evaluate our models in terms of their cross-entropy and their performance in harmonisation experiments. The proposed model o ered higher chord root accuracy than the reference musicological rule-based harmoniser by up to 5% absolute.

Url:

https://hal.inria.fr/hal-00876128

DOI: 10.1080/09298215.2013.822000

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 003150
to stream Hal, to step Curation: 003150
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Le document en format XML

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<front><div type="abstract" xml:lang="en">Most melody harmonisation systems use the generative hidden Markov model (HMM), which model the relation between the hidden chords and the observed melody. Relations to other variables, such as the tonality or the metric struc- ture, are handled by training multiple HMMs or are ignored. In this paper, we propose a discriminative means of combining multiple probabilistic models of various musical variables by means of model interpolation. We evaluate our models in terms of their cross-entropy and their performance in harmonisation experiments. The proposed model o ered higher chord root accuracy than the reference musicological rule-based harmoniser by up to 5% absolute.</div>
</front>
</TEI>
<affiliations><list><country><li>France</li>
<li>Japon</li>
</country>
<region><li>Région Bretagne</li>
</region>
<settlement><li>Rennes</li>
</settlement>
<orgName><li>Université de Rennes 1</li>
<li>Université européenne de Bretagne</li>
</orgName>
</list>
<tree><country name="France"><region name="Région Bretagne"><name sortKey="Raczynski, Stanislaw" sort="Raczynski, Stanislaw" uniqKey="Raczynski S" first="Stanislaw" last="Raczynski">Stanislaw Raczynski</name>
</region>
<name sortKey="Vincent, Emmanuel" sort="Vincent, Emmanuel" uniqKey="Vincent E" first="Emmanuel" last="Vincent">Emmanuel Vincent</name>
</country>
<country name="Japon"><noRegion><name sortKey="Fukayama, Satoru" sort="Fukayama, Satoru" uniqKey="Fukayama S" first="Satoru" last="Fukayama">Satoru Fukayama</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>

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Melody harmonisation with interpolated probabilistic models

Melody harmonisation with interpolated probabilistic models

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