Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915608492244992 |
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| author | Bjare, Mathias Rose Cantisani, Giorgia Pasini, Marco Lattner, Stefan Widmer, Gerhard |
| author_facet | Bjare, Mathias Rose Cantisani, Giorgia Pasini, Marco Lattner, Stefan Widmer, Gerhard |
| contents | We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptual losses, yields encodings that are structured according to a perceptual hierarchy. We demonstrate the emergence of this hierarchical structure by showing that, after training an audio autoencoder in this manner, perceptually salient information is captured in coarser representation structures than with conventional training. Furthermore, we show that such perceptual hierarchies improve latent diffusion decoding in the context of estimating surprisal in music pitches and predicting EEG-brain responses to music listening. Pretrained weights are available on github.com/CPJKU/pa-audioic. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_05350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders Bjare, Mathias Rose Cantisani, Giorgia Pasini, Marco Lattner, Stefan Widmer, Gerhard Sound Artificial Intelligence We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptual losses, yields encodings that are structured according to a perceptual hierarchy. We demonstrate the emergence of this hierarchical structure by showing that, after training an audio autoencoder in this manner, perceptually salient information is captured in coarser representation structures than with conventional training. Furthermore, we show that such perceptual hierarchies improve latent diffusion decoding in the context of estimating surprisal in music pitches and predicting EEG-brain responses to music listening. Pretrained weights are available on github.com/CPJKU/pa-audioic. |
| title | Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2511.05350 |