arXiv:2511.05350v3 Announce Type: replace-cross 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
This paper leverages recent advancements in autoencoder research and perceptually motivated losses to address a core challenge in AI's understanding of complex data like music.
Improving how AI models represent and understand sensory data, particularly in a perceptually aligned manner, is crucial for better AI interaction with the real world, enhancing applications from content generation to human-computer interfaces.
AI's ability to extract and organize perceptually salient information from raw audio now promises to be more efficient and structured, leading to more human-like understanding and generation of complex audio.
- · AI researchers and developers
- · Music technology industry
- · Audio content creators
- · Deep learning frameworks
- · Less perceptually aligned audio processing methods
- · Companies reliant on conventional autoencoder techniques
More efficient and accurate AI models for audio synthesis and analysis emerge.
New applications in personalized sound design, therapeutic audio, and advanced musical instruments become feasible.
The development of AI agents capable of nuanced and creative real-time musical improvisation could accelerate, blurring lines between human and machine artistry.
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Read at arXiv cs.AI