# Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

arXiv: https://arxiv.org/abs/1706.02292 · HTML: https://mlsanity.com/paper/1706.02292/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Learning to Predict Performance-induced Emotion Differences in Classical Piano Music](https://mlsanity.com/paper/2607.28876/) (2026)

## Abstract (arXiv)

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.