Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Preprint 01 Jan 2017Embargo end date: 01 Jan 2017 Finland Publisher:ZenodoJournal:CoRR, volume abs/1706.02292Funded by:EC | EVERYSOUND

Authors: Malik, Miroslav; Adavanne, Sharath; Drossos, Konstantinos; Virtanen, Tuomas; Ticha, Dasa; Jarina, Roman;

doi: 10.48550/arxiv.1706.02292 , 10.5281/zenodo.1401916 , 10.5281/zenodo.1401917

arXiv: 1706.02292

Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

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Abstract

This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.

Accepted for Sound and Music Computing (SMC 2017)

Country

Finland

Related Organizations

Tampere University
Finland

Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), 113, Computer Science - Sound, Machine Learning (cs.LG)

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