The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916410993672192 |
|---|---|
| author | Namgyal, Tashi Hepburn, Alexander Santos-Rodriguez, Raul Laparra, Valero Malo, Jesus |
| author_facet | Namgyal, Tashi Hepburn, Alexander Santos-Rodriguez, Raul Laparra, Valero Malo, Jesus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_17069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification Namgyal, Tashi Hepburn, Alexander Santos-Rodriguez, Raul Laparra, Valero Malo, Jesus Sound Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Audio and Speech Processing 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. |
| title | The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification |
| topic | Sound Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.17069 |