A Framework for Content-Based Search in Large Music Collections
We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We prop...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/6/1/23 |
| _version_ | 1797472824368037888 |
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| author | Tiange Zhu Raphaël Fournier-S’niehotta Philippe Rigaux Nicolas Travers |
| author_facet | Tiange Zhu Raphaël Fournier-S’niehotta Philippe Rigaux Nicolas Travers |
| author_sort | Tiange Zhu |
| collection | DOAJ |
| description | We address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We propose a general framework for building a scalable search engine, based on (i) a music description language that represents music content independently from a specific encoding, (ii) an extendible list of feature-extraction functions, and (iii) indexing, searching, and ranking procedures designed to be integrated into the standard architecture of a text-oriented search engine. As a proof of concept, we also detail an actual implementation of the framework for searching in large collections of XML-encoded music scores, based on the popular ElasticSearch system. It is released as open-source in GitHub, and available as a ready-to-use Docker image for communities that manage large collections of digitized music documents. |
| first_indexed | 2024-03-09T20:06:38Z |
| format | Article |
| id | doaj.art-47f9ee4b36794a4090fda661b41fdaa1 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-09T20:06:38Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-47f9ee4b36794a4090fda661b41fdaa12023-11-24T00:29:06ZengMDPI AGBig Data and Cognitive Computing2504-22892022-02-01612310.3390/bdcc6010023A Framework for Content-Based Search in Large Music CollectionsTiange Zhu0Raphaël Fournier-S’niehotta1Philippe Rigaux2Nicolas Travers3CEDRIC Laboratory, CNAM Paris, 75003 Paris, FranceCEDRIC Laboratory, CNAM Paris, 75003 Paris, FranceCEDRIC Laboratory, CNAM Paris, 75003 Paris, FranceResearch Center, Léonard de Vinci Pôle Universitaire, 92400 Paris La Défense, FranceWe address the problem of scalable content-based search in large collections of music documents. Music content is highly complex and versatile and presents multiple facets that can be considered independently or in combination. Moreover, music documents can be digitally encoded in many ways. We propose a general framework for building a scalable search engine, based on (i) a music description language that represents music content independently from a specific encoding, (ii) an extendible list of feature-extraction functions, and (iii) indexing, searching, and ranking procedures designed to be integrated into the standard architecture of a text-oriented search engine. As a proof of concept, we also detail an actual implementation of the framework for searching in large collections of XML-encoded music scores, based on the popular ElasticSearch system. It is released as open-source in GitHub, and available as a ready-to-use Docker image for communities that manage large collections of digitized music documents.https://www.mdpi.com/2504-2289/6/1/23music collectionsdigital music encodingmusic information retrievalscalable and content-based search |
| spellingShingle | Tiange Zhu Raphaël Fournier-S’niehotta Philippe Rigaux Nicolas Travers A Framework for Content-Based Search in Large Music Collections Big Data and Cognitive Computing music collections digital music encoding music information retrieval scalable and content-based search |
| title | A Framework for Content-Based Search in Large Music Collections |
| title_full | A Framework for Content-Based Search in Large Music Collections |
| title_fullStr | A Framework for Content-Based Search in Large Music Collections |
| title_full_unstemmed | A Framework for Content-Based Search in Large Music Collections |
| title_short | A Framework for Content-Based Search in Large Music Collections |
| title_sort | framework for content based search in large music collections |
| topic | music collections digital music encoding music information retrieval scalable and content-based search |
| url | https://www.mdpi.com/2504-2289/6/1/23 |
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