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

Continuous mid-air hand gesture recognition based on captured hand pose
streams is fundamental for human-computer interaction, particularly in AR / VR.
However, many of the methods proposed to recognize heterogeneous hand gestures
are tested only on the classification task, and the real-time low-latency
gesture segmentation in a continuous stream is not well addressed in the
literature. For this task, we propose the On-Off deep Multi-View Multi-Task
paradigm (OO-dMVMT). The idea is to exploit multiple time-local views related
to hand pose and movement to generate rich gesture descriptions, along with
using heterogeneous tasks to achieve high accuracy. OO-dMVMT extends the
classical MVMT paradigm, where all of the multiple tasks have to be active at
each time, by allowing specific tasks to switch on/off depending on whether
they can apply to the input. We show that OO-dMVMT defines the new SotA on
continuous/online 3D skeleton-based gesture recognition in terms of gesture
classification accuracy, segmentation accuracy, false positives, and decision
latency while maintaining real-time operation.

本文提出一个使用 On-Off 深度多视角多任务范式 (OO-dMVMT) 对捕获的手势姿态流进行连续中空气手势识别的方法，扩展了经典的多视角多任务范式，允许特定任务根据输入开 / 关，并在实时操作的情况下实现了最新的状态下的连续 / 在线三维骨架手势识别。