Article information
| Title | Multi-agent system application for music features extraction, meta-classification and context analysis |
| Authors | Pérez-Marcos, J. , Jiménez-Bravo, D.M. , Paz, J.F. , Villarrubia, G. , Vivian López Batista , Gil, A. B. |
| Summary | 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. |
| Magazine name | Knowledge and Information Systems |
| Magazine number | 62 |
| Initial page | 401 |
| End page | 422 |
| Year | 2020 |
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| Last impact factors | 2.936 (2019) |
| DOI | 10.1007/s10115-018-1319-2 |
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