Dissertation/ Thesis
Knowledge extraction and representation learning for music recommendation and classification
| Title: | Knowledge extraction and representation learning for music recommendation and classification |
|---|---|
| Authors: | Oramas Martín, Sergio |
| Contributors: | University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions |
| Thesis Advisors: | Serra, Xavier |
| Source: | TDX (Tesis Doctorals en Xarxa) |
| Publisher Information: | Universitat Pompeu Fabra, 2017. |
| Publication Year: | 2017 |
| Physical Description: | 177 p. |
| Subject Terms: | Music information retrieval, Recommender systems, Natural language processing, Deep learning, Musicology, Classification, Machine learning, Representation learning, Information extraction, Música, Sistemas de recomendación, Procesado del lenguaje natural, Aprendizaje profundo, Musicología, Clasificación, Aprendizaje automático, Extracción de información |
| Description: | In this thesis, we address the problems of classifying and recommending music present in large collections. We focus on the semantic enrichment of descriptions associated to musical items (e.g., artists biographies, album reviews, metadata), and the exploitation of multimodal data (e.g., text, audio, images). To this end, we first focus on the problem of linking music-related texts with online knowledge repositories and on the automated construction of music knowledge bases. Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation. Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification, combining audio, text, and images. We show how the semantic enrichment of texts and the combination of learned data representations improve the performance on both tasks. |
| Description (Translated): | En esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados. Programa de doctorat en Tecnologies de la Informació i les Comunicacions |
| Document Type: | Dissertation/Thesis |
| File Description: | application/pdf |
| Language: | English |
| Access URL: | http://hdl.handle.net/10803/457709 |
| Rights: | L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Accession Number: | edstdx.10803.457709 |
| Database: | TDX |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://hdl.handle.net/10803/457709# Name: EDS - TDX (ns324271) Category: fullText Text: View record in TDX |
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| Header | DbId: edstdx DbLabel: TDX An: edstdx.10803.457709 RelevancyScore: 1319 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 1318.93640136719 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Knowledge extraction and representation learning for music recommendation and classification – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Oramas+Martín%2C+Sergio%22">Oramas Martín, Sergio</searchLink> – Name: Author Label: Contributors Group: Au Data: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions – Name: Author Label: Thesis Advisors Group: Au Data: Serra, Xavier – Name: TitleSource Label: Source Group: Src Data: TDX (Tesis Doctorals en Xarxa) – Name: Publisher Label: Publisher Information Group: PubInfo Data: Universitat Pompeu Fabra, 2017. – Name: DatePubCY Label: Publication Year Group: Date Data: 2017 – Name: PhysDesc Label: Physical Description Group: PhysDesc Data: 177 p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Music+information+retrieval%22">Music information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Musicology%22">Musicology</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Representation+learning%22">Representation learning</searchLink><br /><searchLink fieldCode="DE" term="%22Information+extraction%22">Information extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Música%22">Música</searchLink><br /><searchLink fieldCode="DE" term="%22Sistemas+de+recomendación%22">Sistemas de recomendación</searchLink><br /><searchLink fieldCode="DE" term="%22Procesado+del+lenguaje+natural%22">Procesado del lenguaje natural</searchLink><br /><searchLink fieldCode="DE" term="%22Aprendizaje+profundo%22">Aprendizaje profundo</searchLink><br /><searchLink fieldCode="DE" term="%22Musicología%22">Musicología</searchLink><br /><searchLink fieldCode="DE" term="%22Clasificación%22">Clasificación</searchLink><br /><searchLink fieldCode="DE" term="%22Aprendizaje+automático%22">Aprendizaje automático</searchLink><br /><searchLink fieldCode="DE" term="%22Extracción+de+información%22">Extracción de información</searchLink> – Name: Abstract Label: Description Group: Ab Data: In this thesis, we address the problems of classifying and recommending music present in large collections. We focus on the semantic enrichment of descriptions associated to musical items (e.g., artists biographies, album reviews, metadata), and the exploitation of multimodal data (e.g., text, audio, images). To this end, we first focus on the problem of linking music-related texts with online knowledge repositories and on the automated construction of music knowledge bases. Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation. Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification, combining audio, text, and images. We show how the semantic enrichment of texts and the combination of learned data representations improve the performance on both tasks. – Name: Abstract Label: Description (Translated) Group: Ab Data: En esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados.<br />Programa de doctorat en Tecnologies de la Informació i les Comunicacions – Name: TypeDocument Label: Document Type Group: TypDoc Data: Dissertation/Thesis – Name: Format Label: File Description Group: SrcInfo Data: application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://hdl.handle.net/10803/457709" linkWindow="_blank">http://hdl.handle.net/10803/457709</link> – Name: Copyright Label: Rights Group: Cpyrght Data: L'accés als continguts d'aquesta tesi queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons: http://creativecommons.org/licenses/by-nc-nd/4.0/ – Name: AN Label: Accession Number Group: ID Data: edstdx.10803.457709 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edstdx&AN=edstdx.10803.457709 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English PhysicalDescription: Pagination: PageCount: 177 Subjects: – SubjectFull: Music information retrieval Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Musicology Type: general – SubjectFull: Classification Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Representation learning Type: general – SubjectFull: Information extraction Type: general – SubjectFull: Música Type: general – SubjectFull: Sistemas de recomendación Type: general – SubjectFull: Procesado del lenguaje natural Type: general – SubjectFull: Aprendizaje profundo Type: general – SubjectFull: Musicología Type: general – SubjectFull: Clasificación Type: general – SubjectFull: Aprendizaje automático Type: general – SubjectFull: Extracción de información Type: general Titles: – TitleFull: Knowledge extraction and representation learning for music recommendation and classification Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Oramas Martín, Sergio IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 11 Type: published Y: 2017 |
| ResultId | 1 |