Abstract
Manual music classification is a slow and costly process. Most recent works about music auto-classification such as genre or emotions make this process easier, but are focused on a single task. In this work, a music multi-classification platform is presented. This platform is based on multi-agent systems, allowing to distribute the extraction, classification, and service tasks among agents. The platform performs a musical genre and emotional classification and provides context information of songs from social networks such as Twitter and Last.fm. The methods chosen based on meta-classifiers to perform single-label and multi-label classification obtain great results. In the case of multi-label classification, better results are obtained than in other previous works.
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Acknowledgements
This work was supported by the Spanish Ministry, Ministerio de Economía y Competitividad and FEDER funds. Project. SURF: Intelligent System for integrated and sustainable management of urban fleets TIN2015-65515-C4-3-R.
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Pérez-Marcos, J., Jiménez-Bravo, D.M., De Paz, J.F. et al. Multi-agent system application for music features extraction, meta-classification and context analysis. Knowl Inf Syst 62, 401–422 (2020). https://doi.org/10.1007/s10115-018-1319-2
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DOI: https://doi.org/10.1007/s10115-018-1319-2