Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders

[Submitted on 7 Nov 2025 (v1), last revised 7 Jul 2026 (this version, v3)]

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Abstract:We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptually motivated losses, yields encodings that are structured according to a perceptual hierarchy. We demonstrate the emergence of this hierarchy 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 pitch surprisal in music and predicting EEG-brain responses to music listening. In both cases, our results surpass those of previous methods. Pretrained weights are available on this http URL.

Submission history

From: Mathias Rose Bjare MSc [view email]
[v1] Fri, 7 Nov 2025 15:44:12 UTC (416 KB)
[v2] Mon, 10 Nov 2025 14:11:02 UTC (416 KB)
[v3] Tue, 7 Jul 2026 12:35:40 UTC (465 KB)