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Journal Articles Big Data and Cognitive Computing Year : 2022

A Framework for Content-Based Search in Large Music Collections

Abstract

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.
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Dates and versions

hal-03678103 , version 1 (25-05-2022)

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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, 2022, 6 (1), pp.23. ⟨10.3390/bdcc6010023⟩. ⟨hal-03678103⟩
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