Predicting Emotions Perceived from Sounds

TL;DR

This paper investigates whether machine learning algorithms can accurately predict emotions perceived from sounds in sonification, demonstrating high prediction accuracy especially with Random Forest regression.

Contribution

It introduces a machine learning approach to predict perceived emotions from sounds, highlighting the effectiveness of feature reduction and Random Forest regression.

Findings

High accuracy in emotion prediction from sounds

Random Forest regression outperforms other algorithms

Effective feature reduction improves prediction performance

Abstract

Sonification is the science of communication of data and events to users through sounds. Auditory icons, earcons, and speech are the common auditory display schemes utilized in sonification, or more specifically in the use of audio to convey information. Once the captured data are perceived, their meanings, and more importantly, intentions can be interpreted more easily and thus can be employed as a complement to visualization techniques. Through auditory perception it is possible to convey information related to temporal, spatial, or some other context-oriented information. An important research question is whether the emotions perceived from these auditory icons or earcons are predictable in order to build an automated sonification platform. This paper conducts an experiment through which several mainstream and conventional machine learning algorithms are developed to study the prediction of emotions perceived from sounds. To do so, the key features of sounds are captured and then are modeled using machine learning algorithms using feature reduction techniques. We observe that it is possible to predict perceived emotions with high accuracy. In particular, the regression based on Random Forest demonstrated its superiority compared to other machine learning algorithms.