Model-Based Deep Learning for Music Information Research

TL;DR

This paper explores integrating traditional music knowledge with deep learning techniques within a differentiable framework to enhance music information research, demonstrating potential applications and benefits.

Contribution

It introduces the concept of model-based deep learning in MIR, combining prior musical knowledge with neural networks for improved research outcomes.

Findings

Illustrated three scenarios of application

Demonstrated integration of prior knowledge into neural networks

Showcased potential benefits of model-based deep learning in MIR

Abstract

In this article, we investigate the notion of model-based deep learning in the realm of music information research (MIR). Loosely speaking, we refer to the term model-based deep learning for approaches that combine traditional knowledge-based methods with data-driven techniques, especially those based on deep learning, within a diff erentiable computing framework. In music, prior knowledge for instance related to sound production, music perception or music composition theory can be incorporated into the design of neural networks and associated loss functions. We outline three specifi c scenarios to illustrate the application of model-based deep learning in MIR, demonstrating the implementation of such concepts and their potential.