# Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks

arXiv: https://arxiv.org/abs/2209.12045 · HTML: https://mlsanity.com/paper/2209.12045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 24 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [On The Differences Between Song and Speech Emotion Recognition: Effect of Feature Sets, Feature Types, and Classifiers](https://mlsanity.com/paper/2004.00200/) (2020)

## Abstract (arXiv)

When songs are composed or performed, there is often an intent by the singer/songwriter of expressing feelings or emotions through it. For humans, matching the emotiveness in a musical composition or performance with the subjective perception of an audience can be quite challenging. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which audio features are extracted to present this information to a data-driven model, that will, in turn, train to predict what is the probability that a given song matches a target emotion. In this paper, we studied the most common features and models used in recent publications to tackle this problem, revealing which ones are best suited for recognizing emotion in a cappella songs.