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Recognizing Music Mood and Theme Using Convolutional Neural Networks and Attention

Alish Dipani, Gaurav Iyer, Veeky Baths
2020 MediaEval Benchmarking Initiative for Multimedia Evaluation  

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We present the UAI-CNRL submission to MediaEval 2020 task on Emotion and Theme Recognition in Music. We make use of the ResNet34 architecture, coupled with a self-attention module to detect moods/themes in music tracks. The autotagging-moodtheme subset of the MTG-Jamendo dataset was used to train the model. We show that the proposed model outperforms the provided VGG-ish and popularity baselines.
dblp:conf/mediaeval/DipaniIB20 fatcat:g22qnc5zvzbdvb3ycyotccczfu
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Alish Dipani, Gaurav Iyer, Veeky Baths. "Recognizing Music Mood and Theme Using Convolutional Neural Networks and Attention." MediaEval Benchmarking Initiative for Multimedia Evaluation (2020)