'Academy and Industry Research Collaboration Center (AIRCC)'
Doi
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