Abstract
The recognition of emotions for annotating large-size music datasets is still an open challenge. The problem lies in that most of the solutions require the audio of the songs and user/expert intervention during certain phases of the recognition process. In this paper, we propose an automatic solution for overcoming these drawbacks. It consists of a heterogeneous set of machine learning models that have been developed from Spotify’s Web data services and miner tools. In order to improve the accuracy of resulting annotations, each model is specialized in recognizing a class of emotions. These models have been validated by using the AcousticBrainz database and have been exported to be integrated into a music emotion recognition system. It has been used to emotionally annotate the Spotify music database which is composed by more than 30 million songs.
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Acknowledgments
This work has been supported by the TIN2015-72241-EXP and TIN2017-84796-C2-2-R projects, granted by the Spanish Ministerio de Economía y Competitividad, and the DisCo-T21-17R and Affective-Lab-T25-17D projects, granted by the Aragonese Government.
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de Quirós, J.G., Baldassarri, S., Beltrán, J.R., Guiu, A., Álvarez, P. (2019). An Automatic Emotion Recognition System for Annotating Spotify’s Songs. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_23
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