MPASCNet improves compressed music restoration with adaptive spectral compression
MPASCNet: Magnitude-Phase-Aware Adaptive Spectral Compression for Compressed Music Restoration
Sound
Summary
Compressed music files like MP3s often lose sound quality because some audio details are removed. The authors developed MPASCNet, a computer model that looks at both the loudness and timing patterns of music to better guess and restore those missing parts. Unlike earlier methods, MPASCNet adapts to the input music instead of using fixed frequency groups, helping it recover cleaner, more natural sound. Tests showed it worked better than some past models while using less computer power.
What this means in practice
- •For audio software developers: Build music players or editors that improve sound clarity by restoring lost details in MP3 and other compressed audio files.
- •For streaming service engineers: Integrate improved audio restoration to enhance the listening experience of streaming platforms using lossy audio formats.
Authors
Xinlong Deng, Yu Xia, Qiuqiang Kong, Yin Cao, Jie Jiang
Abstract
Compressed music restoration aims to recover high-fidelity audio from lossy codecs such as MP3 by restoring missing spectral content and reducing coding artifacts. Recently, generative adversarial networks and band-split models have made progress in compressed music restoration. However, existing methods either operate without explicitly modeling phase or rely on predefined frequency partitions, which may limit restoration performance and the use of input-dependent frequency information. To address these issues, we propose MPASCNet, a Magnitude-Phase-Aware Adaptive Spectral Compression network. Instead of using fixed bands, MPASCNet combines magnitude and phase features to guide cross-attention, adaptively compressing full-resolution spectral features into compact tokens. These tokens are then processed by a spectro-temporal backbone to model spectral-temporal dependencies. Finally, separate decoder branches reconstruct the full-resolution magnitude and wrapped phase spectra. Experiments on MP3 restoration show that MPASCNet outperforms the Apollo and AEROMamba baselines across multiple restoration metrics while using only 6.49 M parameters. The code is available at https://github.com/xinghour/MPASCNet.