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A Framework for Content-Based Search in Large Music Collections
2022
Big Data and Cognitive Computing
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
doi:10.3390/bdcc6010023
fatcat:nm3xvru735fexlo34y2ksdg2ki