An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones.

Crowdsourcing Emotions in Music Domain / Cano, Erion; Morisio, Maurizio. - In: INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE AND APPLICATIONS. - ISSN 0975-900X. - ELETTRONICO. - 8:4(2017), pp. 25-40. [10.5121/ijaia.2017.8403]

Crowdsourcing Emotions in Music Domain

CANO, ERION;MORISIO, MAURIZIO
2017

Abstract

An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2677905
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