# Model-Based Deep Learning for Music Information Research

arXiv: https://arxiv.org/abs/2406.11540 · HTML: https://mlsanity.com/paper/2406.11540/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [F-StrIPE: Fast Structure-Informed Positional Encoding for Symbolic Music Generation](https://mlsanity.com/paper/2502.10491/) (2025)
- [Of All StrIPEs: Investigating Structure-informed Positional Encoding for Efficient Music Generation](https://mlsanity.com/paper/2504.05364/) (2025)
- [Spectrotemporal Modulation: Efficient and Interpretable Feature Representation for Classifying Speech, Music, and Environmental Sounds](https://mlsanity.com/paper/2505.23509/) (2025)

## Abstract (arXiv)

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.