Low-Latency Neural Models for Real-Time Music Enhancement

2026-07-14

AI Summary

This study addresses the challenge of real-time music enhancement under strict causality and low-latency constraints, where music signals in recordings and live streams are commonly degraded by noise, reverberation, and spectral imbalances—conditions poorly handled by existing speech enhancement methods. The work proposes the first benchmark framework specifically designed for real-time music enhancement, integrating degradation-aware modeling, stereo processing, identity-preserving correction, and multidimensional evaluation. Leveraging compact causal neural networks, the authors systematically evaluate various models, including speech baselines, external music denoising systems, offline references, and customized MusicFilterNet-MS variants. Experiments demonstrate that all causal models operate faster than real time; however, enhancement efficacy is highly sensitive to degradation type, dataset, and evaluation metric, with improper processing often degrading audio quality rather than improving it.

Abstract

Music recordings and live streams are often affected by noise, reverberation, spectral imbalances, or artifacts that degrade listening quality. While speech enhancement has matured into a well-defined research area, music enhancement is less established because musical signals combine overlapping sources, wide bandwidths, strong dynamics, and intentional production effects. We study real-time music enhancement under strict causal and low-latency constraints. We formulate the task around recovery of the intended produced mix from acoustic and production-oriented degradations, adapt compact causal networks to music, and compare speech-derived real-time baselines, an external music-denoising model, an offline restoration reference, and a music-specific MusicFilterNet-MS variant. On the tested hardware, all causal models run faster than real time, but improvements depend strongly on the dataset, degradation type, and metric family; under several objective criteria, indiscriminate enhancement can worsen the degraded input. The main contribution is therefore a benchmark and an analysis rather than a universal best model: real-time music enhancement is feasible, but robust improvement requires degradation-aware modeling, stereo-aware processing, identity-preserving correction, and evaluation beyond a single objective score.

Problem

• music enhancement
• low-latency
• real-time processing
• audio degradation
• causal modeling

Innovation

• real-time music enhancement
• low-latency neural models
• causal networks
• degradation-aware modeling
• stereo-aware processing