Reverb plays a critical role in music production, where it provides listeners
with spatial realization, timbre, and texture of the music. Yet, it is
challenging to reproduce the musical reverb of a reference music track even by
skilled engineers. In response, we propose an end-to-end system capable of
switching the musical reverb factor of two different mixed vocal tracks. This
method enables us to apply the reverb of the reference track to the source
track to which the effect is desired. Further, our model can perform
de-reverberation when the reference track is used as a dry vocal source. The
proposed model is trained in combination with an adversarial objective, which
makes it possible to handle high-resolution audio samples. The perceptual
evaluation confirmed that the proposed model can convert the reverb factor with
the preferred rate of 64.8%. To the best of our knowledge, this is the first
attempt to apply deep neural networks to converting music reverb of vocal
tracks.

提出了一种使用深度神经网络转换音乐人声曲目的混响因子的端到端系统。该模型可处理高分辨率音频样本，并以 64.8％的首选率成功进行了知觉评估。

使用端到端卷积深度神经网络进行混音人声轨道的混响转换

Reverb Conversion of Mixed Vocal Tracks Using an End-to-end  Convolutional Deep Neural Network

Our main contributions are as follows: (I) We demonstrate the first attempt
to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced
dataset of audio samples, achieving ROC-AUC of 0.846; (II) Our model, the
COVID-19 Identification ResNet, (CIdeR), has potential for rapid scalability,
minimal cost and improving performance as more data becomes available. This
could enable regular COVID-19 testing at apopulation scale; (III) We introduce
a novel modelling strategy using a custom deep neural network to diagnose
COVID-19 from a joint breath and cough representation; (IV) We release our four
stratified folds for cross parameter optimisation and validation on a standard
public corpus and details on the models for reproducibility and future
reference.

本研究利用自发集成的数据集，首次尝试使用端到端的深度学习方法对 COVID-19 进行诊断，ROC-AUC 为 0.846；引入自定义的深度神经网络，以关节呼吸和咳嗽表示的方式诊断 COVID-19；提供了四个分层折叠的数据集以及模型细节，以进行交叉参数优化和验证，并具备可重现性。

通过呼吸和咳嗽声音进行端到端 COVID-19 检测

End-2-End COVID-19 Detection from Breath & Cough Audio

This paper introduces WaveGrad, a conditional model for waveform generation
which estimates gradients of the data density. The model is built on prior work
on score matching and diffusion probabilistic models. It starts from a Gaussian
white noise signal and iteratively refines the signal via a gradient-based
sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to
trade inference speed for sample quality by adjusting the number of refinement
steps, and bridges the gap between non-autoregressive and autoregressive models
in terms of audio quality. We find that it can generate high fidelity audio
samples using as few as six iterations. Experiments reveal WaveGrad to generate
high fidelity audio, outperforming adversarial non-autoregressive baselines and
matching a strong likelihood-based autoregressive baseline using fewer
sequential operations. Audio samples are available at
this https URL

WaveGrad 是一种基于分数匹配和扩散概率模型生成波形的条件模型，它使用梅尔频谱来限制采样过程，并可通过调整迭代次数来平衡生成质量和速度。实验结果表明 WaveGrad 能够生成高保真度的音频样本，超越对抗样本和自回归模型，并通过少量的循环操作匹配了强度的似然自回归模型。