Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet

arXiv:2402.17701v1 Announce Type: cross
Abstract: There have been significant advances in deep learning for music demixing in recent years. However, there has been little attention given to how these neural networks can be adapted for real-time low-latency applications, which could be helpful for hearing aids, remixing audio streams and live shows. In this paper, we investigate the various challenges involved in adapting current demixing models in the literature for this use case. Subsequently, inspired by the Hybrid Demucs architecture, we …

abstract advances applications arxiv attention audio cs.lg cs.sd deep learning eess.as hearing hybrid latency low music networks neural networks real-time shows spectrogram type