A Modulation Front-End for Music Audio Tagging

TL;DR

This paper introduces a learned modulation front-end for music audio tagging that enhances feature extraction by mimicking perceptually motivated filter banks, leading to interpretable and effective representations.

Contribution

It proposes end-to-end learned modulation filter bank front-ends, ModNet and SincModNet, for improved music tagging without relying on extensive domain knowledge.

Findings

Modulation filtering yields promising tagging performance.

The approach offers visualizable and interpretable audio representations.

Performance is evaluated on the MagnaTagATune dataset.

Abstract

Convolutional Neural Networks have been extensively explored in the task of automatic music tagging. The problem can be approached by using either engineered time-frequency features or raw audio as input. Modulation filter bank representations that have been actively researched as a basis for timbre perception have the potential to facilitate the extraction of perceptually salient features. We explore end-to-end learned front-ends for audio representation learning, ModNet and SincModNet, that incorporate a temporal modulation processing block. The structure is effectively analogous to a modulation filter bank, where the FIR filter center frequencies are learned in a data-driven manner. The expectation is that a perceptually motivated filter bank can provide a useful representation for identifying music features. Our experimental results provide a fully visualisable and interpretable front-end temporal modulation decomposition of raw audio. We evaluate the performance of our model against the state-of-the-art of music tagging on the MagnaTagATune dataset. We analyse the impact on performance for particular tags when time-frequency bands are subsampled by the modulation filters at a progressively reduced rate. We demonstrate that modulation filtering provides promising results for music tagging and feature representation, without using extensive musical domain knowledge in the design of this front-end.