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

Autor:	Namgyal, Tashi, Hepburn, Alexander, Santos-Rodriguez, Raul, Laparra, Valero, Malo, Jesus
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:2312.03455
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.17069 Zobrazit plný text záznamu View this record from Arxiv