%0 Journal Article %1 Pérez-Marcos2020 %A Perez-Marcos, Javier %A Jimenez-Bravo, Diego M. %A De Paz, Juan F. %A Villarrubia Gonzalez, Gabriel %A Lopez, Vivian F. %A Gil, Ana B. %D 2020 %J Knowledge and Information Systems %K Auto-classification Social Performance Multi-label analysis intelligence classification Meta-classifiers Single-label Distributed Emotional Music processing Genre information Artificial Twitter recommendation learning networks from:kamber Machine Multi-agent systems Contextual Last.fm evaluation Multi-classification %N 1 %P 401--422 %R 10.1007/s10115-018-1319-2 %T Multi-agent system application for music features extraction, meta-classification and context analysis %U https://doi.org/10.1007/s10115-018-1319-2 %V 62 %X 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.

@article{Pérez-Marcos2020, 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.}, added-at = {2024-03-27T09:25:06.000+0100}, author = {Perez-Marcos, Javier and Jimenez-Bravo, Diego M. and De Paz, Juan F. and Villarrubia Gonzalez, Gabriel and Lopez, Vivian F. and Gil, Ana B.}, biburl = {https://www.bibsonomy.org/bibtex/2cf06b2a1320b8c317491949cdcf1b261/sop2-ffzg}, day = 01, doi = {10.1007/s10115-018-1319-2}, interhash = {24178c54da3b1366db95ef27af56241c}, intrahash = {cf06b2a1320b8c317491949cdcf1b261}, issn = {0219-3116}, journal = {Knowledge and Information Systems}, keywords = {Auto-classification Social Performance Multi-label analysis intelligence classification Meta-classifiers Single-label Distributed Emotional Music processing Genre information Artificial Twitter recommendation learning networks from:kamber Machine Multi-agent systems Contextual Last.fm evaluation Multi-classification}, month = jan, number = 1, pages = {401--422}, timestamp = {2024-03-27T09:25:06.000+0100}, title = {Multi-agent system application for music features extraction, meta-classification and context analysis}, url = {https://doi.org/10.1007/s10115-018-1319-2}, volume = 62, year = 2020 }