An exhaustive variable selection study for linear models of soundscape emotions: rankings and Gibbs analysis

TL;DR

This study conducts an exhaustive variable selection for linear models predicting soundscape emotions, introducing novel Gibbs sampling techniques and achieving parsimonious models with high predictive accuracy.

Contribution

It presents a comprehensive variable selection methodology, including Gibbs sampling, for soundscape emotion models, improving interpretability and performance over classical methods.

Findings

Two simple linear models with 7 and 16 variables respectively.

Models achieve R^2 > 0.86 and R^2 > 0.63 after cross-validation.

Gibbs sampling provides clearer variable relevance insights.

Abstract

In the last decade, soundscapes have become one of the most active topics in Acoustics, providing a holistic approach to the acoustic environment, which involves human perception and context. Soundscapes-elicited emotions are central and substantially subtle and unnoticed (compared to speech or music). Currently, soundscape emotion recognition is a very active topic in the literature. We provide an exhaustive variable selection study (i.e., a selection of the soundscapes indicators) to a well-known dataset (emo-soundscapes). We consider linear soundscape emotion models for two soundscapes descriptors: arousal and valence. Several ranking schemes and procedures for selecting the number of variables are applied. We have also performed an alternating optimization scheme for obtaining the best sequences keeping fixed a certain number of features. Furthermore, we have designed a novel technique based on Gibbs sampling, which provides a more complete and clear view of the relevance of each variable. Finally, we have also compared our results with the analysis obtained by the classical methods based on p-values. As a result of our study, we suggest two simple and parsimonious linear models of only 7 and 16 variables (within the 122 possible features) for the two outputs (arousal and valence), respectively. The suggested linear models provide very good and competitive performance, with $R^2>0.86$ and $R^2>0.63$ (values obtained after a cross-validation procedure), respectively.