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Multi-agent system application for music features extraction, meta-classification and context analysis

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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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Authors and Affiliations

  1. Department of Computer Science and Automatic, University of Salamanca, Plaza de la Merced, s/n, 37008, Salamanca, Spain

    Javier Pérez-Marcos, Diego M. Jiménez-Bravo, Juan F. De Paz, Gabriel Villarrubia González, Vivian F. López & Ana B. Gil

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  1. Javier Pérez-Marcos
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  2. Diego M. Jiménez-Bravo
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  3. Juan F. De Paz
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Correspondence to Javier Pérez-Marcos.

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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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