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

TL;DR

This paper compares the effectiveness of various audio features and neural network models in recognizing emotions in a cappella songs, aiming to identify the most suitable approaches for this task.

Contribution

It provides a performance comparison of common audio features and neural network models for emotion recognition in a cappella music, highlighting the most effective combinations.

Findings

Certain audio features outperform others in emotion recognition accuracy.

Neural network models show varying effectiveness depending on feature selection.

The study identifies optimal feature-model pairings for emotion detection in a cappella songs.

Abstract

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.