Learning regular grammars to model musical style: Comparing different coding schemes
An application of Grammatical Inference (GI) in the field of Music Processing is presented, were Regular Grammars are used for modeling musical style. The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style...
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| creator | Cruz-Alcázar, Pedro P. Vidal-Ruiz, Enrique |
| description | An application of Grammatical Inference (GI) in the field of Music Processing is presented, were Regular Grammars are used for modeling musical style. The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved. |
| doi_str_mv | 10.1007/BFb0054077 |
| format | Conference Proceeding |
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The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540647767</identifier><identifier>ISBN: 9783540647768</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540687076</identifier><identifier>EISBN: 9783540687078</identifier><identifier>DOI: 10.1007/BFb0054077</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Average Success Rate ; Code Scheme ; Computer science; control theory; systems ; Exact sciences and technology ; Grammatical Inference ; Musical Style ; Speech and sound recognition and synthesis. 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The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Average Success Rate</subject><subject>Code Scheme</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Grammatical Inference</subject><subject>Musical Style</subject><subject>Speech and sound recognition and synthesis. Linguistics</subject><subject>Symbol String</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540647767</isbn><isbn>9783540647768</isbn><isbn>3540687076</isbn><isbn>9783540687078</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFUMtOw0AMXF4SpfTCF-yBA5eAN052E25QUUCqxKWcI2fjLYG8tFsO_XsStRK-2LLHo5kR4kbBvQIwD8-rEiBNwJgTcYXjoDMDRp-KmdJKRYhJfnY8JMZocy5mgBBHuUnwUixC-IaxMMbU5DOxWTP5ru620vP2tyEvt57alnyQu162fcWNbH9DbamRYbdv-FEu-3YgP71UtXPsudtJ21fTItgvbjlciwtHTeDFsc_F5-pls3yL1h-v78undWSVQRORUgip0pWOyTlktpgya12qxLFWLqHRpc5yKNlVYLLcpWyBiGKMLdoS5-L2wDtQGAU6T52tQzH4ejSwL-I4M4nWI-zuAAvDJJt9Ufb9TygUFFOixX-i-AcfV2Rm</recordid><startdate>20060523</startdate><enddate>20060523</enddate><creator>Cruz-Alcázar, Pedro P.</creator><creator>Vidal-Ruiz, Enrique</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>20060523</creationdate><title>Learning regular grammars to model musical style: Comparing different coding schemes</title><author>Cruz-Alcázar, Pedro P. ; Vidal-Ruiz, Enrique</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1737-a1130516d62aff3eec35ee66b14fe61f4a4076890befd0789f5ec0aaa232c3cb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Average Success Rate</topic><topic>Code Scheme</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Grammatical Inference</topic><topic>Musical Style</topic><topic>Speech and sound recognition and synthesis. Linguistics</topic><topic>Symbol String</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cruz-Alcázar, Pedro P.</creatorcontrib><creatorcontrib>Vidal-Ruiz, Enrique</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cruz-Alcázar, Pedro P.</au><au>Vidal-Ruiz, Enrique</au><au>Honavar, Vasant</au><au>Slutzki, Giora</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Learning regular grammars to model musical style: Comparing different coding schemes</atitle><btitle>Grammatical Inference</btitle><date>2006-05-23</date><risdate>2006</risdate><spage>211</spage><epage>222</epage><pages>211-222</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540647767</isbn><isbn>9783540647768</isbn><eisbn>3540687076</eisbn><eisbn>9783540687078</eisbn><abstract>An application of Grammatical Inference (GI) in the field of Music Processing is presented, were Regular Grammars are used for modeling musical style. The interest in modeling musical style resides in the use of these models in applications, such as Automatic Composition and Automatic Musical Style Recognition. We have studied three GI Algorithms, which have been previously applied successfully in other fields. In this work, these algorithms have been used to learn a stochastic grammar for each of three different musical styles from examples of melodies. Then, each of the learned grammars was used to stochastically synthesize new melodies (Composition) or to classify test melodies (Style Recognition). Our previous studies in this field showed the need of a proper music coding scheme. Different coding schemes are presented and compared according to results in Composition and Style Recognition. Results from previous studies have been improved.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/BFb0054077</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Grammatical Inference, 2006, p.211-222 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_2287466 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Average Success Rate Code Scheme Computer science control theory systems Exact sciences and technology Grammatical Inference Musical Style Speech and sound recognition and synthesis. Linguistics Symbol String |
| title | Learning regular grammars to model musical style: Comparing different coding schemes |
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