Towards Practical Real-Time Low-Latency Music Source Separation
Abstract
In recent years, significant progress has been made in the field of deep learning for music demixing. However, there has been limited attention on real-time, low-latency music demixing, which holds potential for various applications, such as hearing aids, audio stream remixing, and live performances. Additionally, a notable tendency has emerged towards the development of larger models, limiting their applicability in certain scenarios. In this paper, we introduce a lightweight real-time low-latency model called Real-Time Single-Path TFC-TDF UNET (RT-STT), which is based on the Dual-Path TFC-TDF UNET (DTTNet). In RT-STT, we propose a feature fusion technique based on channel expansion. We also demonstrate the superiority of single-path modeling over dual-path modeling in real-time models. Moreover, we investigate the method of quantization to further reduce inference time. RT-STT exhibits superior performance with significantly fewer parameters and shorter inference times compared to state-of-the-art models.
Cite
@article{arxiv.2511.13146,
title = {Towards Practical Real-Time Low-Latency Music Source Separation},
author = {Junyu Wu and Jie Liu and Tianrui Pan and Jie Tang and Gangshan Wu},
journal= {arXiv preprint arXiv:2511.13146},
year = {2025}
}