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arXiv · 1911.05833

Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs

Khaled Koutini·Shreyan Chowdhury·Verena Haunschmid·Hamid Eghbal-zadeh·Gerhard Widmer
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Abstract

We present CP-JKU submission to MediaEval 2019; a Receptive Field-(RF)-regularized and Frequency-Aware CNN approach for tagging music with emotion/mood labels. We perform an investigation regarding the impact of the RF of the CNNs on their performance on this dataset. We observe that ResNets with smaller receptive fields -- originally adapted for acoustic scene classification -- also perform well in the emotion tagging task. We improve the performance of such architectures using techniques such as Frequency Awareness and Shake-Shake regularization, which were used in previous work on general acoustic recognition tasks.

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Khaled Koutini, Shreyan Chowdhury, Verena Haunschmid, Hamid Eghbal-zadeh, Gerhard Widmer. 2019-10-28. Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs. https://arxiv.org/abs/1911.05833

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