BWSNet: Automatic Perceptual Assessment of Audio Signals

TL;DR

BWSNet is a novel model that learns perceptual audio attributes from raw human judgments using BWS data, mapping sounds into an embedded space aligned with human perception.

Contribution

It introduces a set of cost functions and constraints for metric learning from BWS data, effectively capturing perceptual attributes in a latent space.

Findings

Latent space structure aligns with human judgments.

Effective in modeling perception of speech social attitudes.

Accurate in representing timbral qualities.

Abstract

This paper introduces BWSNet, a model that can be trained from raw human judgements obtained through a Best-Worst scaling (BWS) experiment. It maps sound samples into an embedded space that represents the perception of a studied attribute. To this end, we propose a set of cost functions and constraints, interpreting trial-wise ordinal relations as distance comparisons in a metric learning task. We tested our proposal on data from two BWS studies investigating the perception of speech social attitudes and timbral qualities. For both datasets, our results show that the structure of the latent space is faithful to human judgements.