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 propose the Hybrid Spectrogram Time-domain
Audio Separation Network HS-TasNet, which utilises the advantages of spectral
and waveform domains. For a latency of 23 ms, the HS-TasNet obtains an overall
signal-to-distortion ratio (SDR) of 4.65 on the MusDB test set, and increases
to 5.55 with additional training data. These results demonstrate the potential
of efficient demixing for real-time low-latency music applications.

通过借鉴 Hybrid Demucs 架构，本文提出了混合谱图时域音频分离网络（HS-TasNet），结合了频谱和波形域的优势，为实时低延迟的音乐应用展现了高效分离的潜力。

实时低延迟音乐源分离使用混合的谱图 - TasNet

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

In this report, we present our award-winning solutions for the Music Demixing
Track of Sound Demixing Challenge 2023. We focus on two methods designed for
this challenge: a time-efficient source separation network that achieves
state-of-the-art results on the MUSDB benchmark and a loss masking method for
noise-robust source separation. Code for reproducing model training and final
submissions is available at github.com/kuielab/sdx23.

本文介绍了我们在 2023 年声音分离比赛的音乐分离赛道中获奖的两种解决方案，分别使用了一种时间有效的源分离网络和一种用于噪音鲁棒源分离的损失掩蔽方法。

2023 声音分离挑战 -- 音乐分离赛道技术报告

Sound Demixing Challenge 2023 -- Music Demixing Track Technical Report

Music demixing is the task of separating different tracks from the given
single audio signal into components, such as drums, bass, and vocals from the
rest of the accompaniment. Separation of sources is useful for a range of
areas, including entertainment and hearing aids. In this paper, we introduce
two new benchmarks for the sound source separation tasks and compare popular
models for sound demixing, as well as their ensembles, on these benchmarks. For
the models' assessments, we provide the leaderboard at
this https URL, giving a comparison for a range of models.
The new benchmark datasets are available for download. We also develop a novel
approach for audio separation, based on the ensembling of different models that
are suited best for the particular stem. The proposed solution was evaluated in
the context of the Music Demixing Challenge 2023 and achieved top results in
different tracks of the challenge. The code and the approach are open-sourced
on GitHub.

本文旨在介绍音乐分离的任务以及该任务的两个新的基准数据集，比较流行模型的表现并提供评估排名，同时提供可下载的基准数据集。作者提出了一种使用不同模型进行集成的新方法，并在音乐分离挑战赛中取得了最佳结果，该方法的代码和技术细节已在 GitHub 上公开。