Playing Mozart phrase by phrase
Identifieur interne :
000197 ( PascalFrancis/Corpus );
précédent :
000196;
suivant :
000198
Playing Mozart phrase by phrase
Auteurs : Asmir Tobudic ;
Gerhard WidmerSource :
-
Lecture notes in computer science [ 0302-9743 ] ; 2003.
RBID : Pascal:04-0161508
Descripteurs français
English descriptors
Abstract
The article presents an application of instance-based learning to the problem of expressive music performance. A system is described that tries to learn to shape tempo and dynamics of a musical performance by analogy to timing and dynamics patterns found in performances by a concert pianist. The learning algorithm itself is a straightforward k-nearest-neighbour algorithm. The interesting aspects of this work are application-specific: we show how a complex, multi-level artifact like the tempo/dynamics variations applied by a musician can be decomposed into well-defined training examples for a learner, and that case-based learning is indeed a sensible strategy in an artistic domain like music performance. While the results of a first quantitative experiment turn out to be rather disappointing, we will show various ways in which the results can be improved, finally resulting in a system that won a prize in a recent 'computer music performance' contest.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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| A08 | 01 | 1 | ENG | @1 Playing Mozart phrase by phrase |
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| A09 | 01 | 1 | ENG | @1 Case-based reasoning research and development : Trondheim, 23-26 June 2003 |
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| A11 | 01 | 1 | | @1 TOBUDIC (Asmir) |
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| A11 | 02 | 1 | | @1 WIDMER (Gerhard) |
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| A12 | 01 | 1 | | @1 ASHLEY (Kevin D.) @9 ed. |
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| A12 | 02 | 1 | | @1 BRIDGE (Derek G.) @9 ed. |
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| A14 | 01 | | | @1 Austrian Research Institute for Artificial Intelligence @2 Vienna @3 AUT @Z 1 aut. @Z 2 aut. |
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| A14 | 02 | | | @1 Department of Medical Cybernetics and Artificial Intelligence, University of Vienna @3 AUT @Z 2 aut. |
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| C01 | 01 | | ENG | @0 The article presents an application of instance-based learning to the problem of expressive music performance. A system is described that tries to learn to shape tempo and dynamics of a musical performance by analogy to timing and dynamics patterns found in performances by a concert pianist. The learning algorithm itself is a straightforward k-nearest-neighbour algorithm. The interesting aspects of this work are application-specific: we show how a complex, multi-level artifact like the tempo/dynamics variations applied by a musician can be decomposed into well-defined training examples for a learner, and that case-based learning is indeed a sensible strategy in an artistic domain like music performance. While the results of a first quantitative experiment turn out to be rather disappointing, we will show various ways in which the results can be improved, finally resulting in a system that won a prize in a recent 'computer music performance' contest. |
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Format Inist (serveur)
| NO : | PASCAL 04-0161508 INIST |
|---|
| ET : | Playing Mozart phrase by phrase |
|---|
| AU : | TOBUDIC (Asmir); WIDMER (Gerhard); ASHLEY (Kevin D.); BRIDGE (Derek G.) |
|---|
| AF : | Austrian Research Institute for Artificial Intelligence/Vienna/Autriche (1 aut., 2 aut.); Department of Medical Cybernetics and Artificial Intelligence, University of Vienna/Autriche (2 aut.) |
|---|
| DT : | Publication en série; Congrès; Niveau analytique |
|---|
| SO : | Lecture notes in computer science; ISSN 0302-9743; Allemagne; Da. 2003; Vol. 2689; Pp. 552-566; Bibl. 13 ref. |
|---|
| LA : | Anglais |
|---|
| EA : | The article presents an application of instance-based learning to the problem of expressive music performance. A system is described that tries to learn to shape tempo and dynamics of a musical performance by analogy to timing and dynamics patterns found in performances by a concert pianist. The learning algorithm itself is a straightforward k-nearest-neighbour algorithm. The interesting aspects of this work are application-specific: we show how a complex, multi-level artifact like the tempo/dynamics variations applied by a musician can be decomposed into well-defined training examples for a learner, and that case-based learning is indeed a sensible strategy in an artistic domain like music performance. While the results of a first quantitative experiment turn out to be rather disappointing, we will show various ways in which the results can be improved, finally resulting in a system that won a prize in a recent 'computer music performance' contest. |
|---|
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| FD : | Intelligence artificielle; Système temporisé; Artefact; Raisonnement basé sur cas; Musique; Acoustique musicale; Plus proche voisin; 4375 |
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| ED : | Artificial intelligence; Timed system; Artefact; Case based reasoning; Music; Musical acoustics; Nearest neighbour |
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| SD : | Inteligencia artificial; Sistema temporizado; Artefacto; Razonamiento fundado sobre caso; Música; Acústica musical; Vecino más cercano |
|---|
| LO : | INIST-16343.354000117776960390 |
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| ID : | 04-0161508 |
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Links to Exploration step
Pascal:04-0161508
Le document en format XML
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<ET>Playing Mozart phrase by phrase</ET>
<AU>TOBUDIC (Asmir); WIDMER (Gerhard); ASHLEY (Kevin D.); BRIDGE (Derek G.)</AU>
<AF>Austrian Research Institute for Artificial Intelligence/Vienna/Autriche (1 aut., 2 aut.); Department of Medical Cybernetics and Artificial Intelligence, University of Vienna/Autriche (2 aut.)</AF>
<DT>Publication en série; Congrès; Niveau analytique</DT>
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<EA>The article presents an application of instance-based learning to the problem of expressive music performance. A system is described that tries to learn to shape tempo and dynamics of a musical performance by analogy to timing and dynamics patterns found in performances by a concert pianist. The learning algorithm itself is a straightforward k-nearest-neighbour algorithm. The interesting aspects of this work are application-specific: we show how a complex, multi-level artifact like the tempo/dynamics variations applied by a musician can be decomposed into well-defined training examples for a learner, and that case-based learning is indeed a sensible strategy in an artistic domain like music performance. While the results of a first quantitative experiment turn out to be rather disappointing, we will show various ways in which the results can be improved, finally resulting in a system that won a prize in a recent 'computer music performance' contest.</EA>
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