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

arXiv: https://arxiv.org/abs/1911.05833 · HTML: https://mlsanity.com/paper/1911.05833/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.12395/) (2021)
- [Tracing Back Music Emotion Predictions to Sound Sources and Intuitive Perceptual Qualities](https://mlsanity.com/paper/2106.07787/) (2021)
- [OpenMU: Your Swiss Army Knife for Music Understanding](https://mlsanity.com/paper/2410.15573/) (2024)
- [Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features via Acoustic Domain Adaptation](https://mlsanity.com/paper/2102.13479/) (2021)
- [MIRFLEX: Music Information Retrieval Feature Library for Extraction](https://mlsanity.com/paper/2411.00469/) (2024)

## Abstract (arXiv)

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.