Perceptual Musical Features for Interpretable Audio Tagging

TL;DR

This paper introduces an interpretable music tagging approach combining symbolic knowledge, neural networks, and perceptual features, achieving competitive results while enhancing transparency in audio classification.

Contribution

The study presents a novel workflow integrating multiple interpretability techniques for music tagging, addressing the lack of interpretability in neural network-based audio classification.

Findings

Outperforms baseline models on MTG-Jamendo and GTZAN datasets

Achieves competitive performance with state-of-the-art methods

Highlights the value of interpretability over marginal performance loss

Abstract

In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging to elucidate their output for a given input. While the issue of interpretability has been emphasized in other fields like medicine, it has not received attention in music-related tasks. In this study, we explored the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques: a) leveraging symbolic knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal processing to extract perceptual features from audio files. These features were subsequently used to train an interpretable machine-learning model for tag prediction. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset. Our method surpassed the performance of baseline models in both tasks and, in certain instances, demonstrated competitiveness with the current state-of-the-art. We conclude that there are use cases where the deterioration in performance is outweighed by the value of interpretability.