Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs
This work addresses music tagging for emotion/mood labels, but it is incremental as it applies existing techniques to a specific dataset.
The paper tackled emotion and theme recognition in music by adapting ResNets with smaller receptive fields, originally for acoustic scene classification, and improved performance using frequency awareness and Shake-Shake regularization, achieving results for the MediaEval 2019 submission.
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.