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

  • Javier Pérez-MarcosEmail author
  • Diego M. Jiménez-Bravo
  • Juan F. De Paz
  • Gabriel Villarrubia González
  • Vivian F. López
  • Ana B. Gil
Regular Paper
  • 26 Downloads

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.

Keywords

Music classification Multi-agent system Multi-label classification Meta-classifiers Musical genre Musical emotions Social networks 

Notes

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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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and AutomaticUniversity of SalamancaSalamancaSpain

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