# The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification

arXiv: https://arxiv.org/abs/2409.17069 · HTML: https://mlsanity.com/paper/2409.17069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Evaluating generative audio systems and their metrics](https://mlsanity.com/paper/2209.00130/) (2022)
- [On the relation between statistical learning and perceptual distances](https://mlsanity.com/paper/2106.04427/) (2021)
- [The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use](https://mlsanity.com/paper/1306.1461/) (2013)
- [Enforcing Perceptual Consistency on Generative Adversarial Networks by Using the Normalised Laplacian Pyramid Distance](https://mlsanity.com/paper/1908.04347/) (2019)
- [Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data](https://mlsanity.com/paper/2312.03455/) (2023)

## Abstract (arXiv)

The subjective quality of natural signals can be approximated with objective perceptual metrics. Designed to approximate the perceptual behaviour of human observers, perceptual metrics often reflect structures found in natural signals and neurological pathways. Models trained with perceptual metrics as loss functions can capture perceptually meaningful features from the structures held within these metrics. We demonstrate that using features extracted from autoencoders trained with perceptual losses can improve performance on music understanding tasks, i.e. genre classification, over using these metrics directly as distances when learning a classifier. This result suggests improved generalisation to novel signals when using perceptual metrics as loss functions for representation learning.