Applying Topological Persistence in Convolutional Neural Network for Music Audio Signals

TL;DR

This paper introduces a novel method that integrates topological persistence, specifically persistence landscapes, into CNNs to improve music audio signal classification, demonstrating superior accuracy over existing models.

Contribution

It presents the first integration of persistent homology into CNNs for audio signals, enhancing shape-based feature extraction in deep learning models.

Findings

Persistent CNN outperforms state-of-the-art models in music tagging.

Embedding topological summaries improves feature representation.

The model provides insights into shape-based features learned by neural networks.

Abstract

Recent years have witnessed an increased interest in the application of persistent homology, a topological tool for data analysis, to machine learning problems. Persistent homology is known for its ability to numerically characterize the shapes of spaces induced by features or functions. On the other hand, deep neural networks have been shown effective in various tasks. To our best knowledge, however, existing neural network models seldom exploit shape information. In this paper, we investigate a way to use persistent homology in the framework of deep neural networks. Specifically, we propose to embed the so-called "persistence landscape," a rather new topological summary for data, into a convolutional neural network (CNN) for dealing with audio signals. Our evaluation on automatic music tagging, a multi-label classification task, shows that the resulting persistent convolutional neural network (PCNN) model can perform significantly better than state-of-the-art models in prediction accuracy. We also discuss the intuition behind the design of the proposed model, and offer insights into the features that it learns.