Back to Ear: Perceptually Driven High Fidelity Music Reconstruction

• URL: http://arxiv.org/abs/2509.14912v2
• Date: Thu, 06 Nov 2025 07:21:38 GMT
• Title: Back to Ear: Perceptually Driven High Fidelity Music Reconstruction
• Authors: Kangdi Wang, Zhiyue Wu, Dinghao Zhou, Rui Lin, Junyu Dai, Tao Jiang,
• Abstract summary: epsilonar-VAE is an open-source music signal reconstruction model that rethinks and optimize the Variational Autoencoders (VAEs)<n> Experiments show epsilonar-VAE at 44.1kHz substantially outperforms leading open-source models across diverse metrics.
• Score: 4.380428073231143
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Variational Autoencoders (VAEs) are essential for large-scale audio tasks like diffusion-based generation. However, existing open-source models often neglect auditory perceptual aspects during training, leading to weaknesses in phase accuracy and stereophonic spatial representation. To address these challenges, we propose {\epsilon}ar-VAE, an open-source music signal reconstruction model that rethinks and optimizes the VAE training paradigm. Our contributions are threefold: (i) A K-weighting perceptual filter applied prior to loss calculation to align the objective with auditory perception. (ii) Two novel phase losses: a Correlation Loss for stereo coherence, and a Phase Loss using its derivatives--Instantaneous Frequency and Group Delay--for precision. (iii) A new spectral supervision paradigm where magnitude is supervised by all four Mid/Side/Left/Right components, while phase is supervised only by the LR components. Experiments show {\epsilon}ar-VAE at 44.1kHz substantially outperforms leading open-source models across diverse metrics, showing particular strength in reconstructing high-frequency harmonics and the spatial characteristics.

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