q-fin金融，共计2篇
cs.SD语音，共计5篇
eess.AS音频处理，共计7篇
1.q-fin金融:
【1】 A fast Monte Carlo scheme for additive processes and option pricing 标题：可加过程和期权定价的快速蒙特卡罗方法 链接：https://arxiv.org/abs/2112.08291
作者：Michele Azzone,Roberto Baviera 摘要：在本文中，我们提出了一种用于加法过程的快速蒙特卡罗方法。我们详细分析了数值误差源，并提出了一种减少两个主要误差源的技术。我们还将我们的结果与基准方法进行了比较：高斯近似下的跳跃模拟。我们展示了加性正常回火稳定过程的一个应用，这是一类加性过程，它“精确地”校准了隐含的挥发性表面。数值结果是相关的。该算法是一个精确的工具，用于定价路径相关的离散监控期权，误差小于等于1 bp。该方案也很快：计算时间与布朗运动的标准算法的数量级相同。 摘要：In this paper, we present a fast Monte Carlo scheme for additive processes. We analyze in detail numerical error sources and propose a technique that reduces the two major sources of error. We also compare our results with a benchmark method: the jump simulation with Gaussian approximation. We show an application to additive normal tempered stable processes, a class of additive processes that calibrates "exactly" the implied volatility surface. Numerical results are relevant. The algorithm is an accurate tool for pricing path-dependent discretely-monitoring options with errors of one bp or below. The scheme is also fast: the computational time is of the same order of magnitude of standard algorithms for Brownian motions.
【2】 Stock prices and Macroeconomic indicators: Investigating a correlation in Indian context 标题：股票价格与宏观经济指标：在印度背景下的相关性研究 链接：https://arxiv.org/abs/2112.08071
作者：Dhruv Rawat,Sujay Patni,Ram Mehta 摘要：本文的目的是发现股票市场价格与基本宏观经济指标之间存在关系。我们构建了一个向量自回归（VAR）模型，包括九个主要宏观经济指标（利率、通胀、汇率、货币供应量、gdp、fdi、贸易gdp比率、石油价格、黄金价格），然后尝试对未来5年进行预测。最后，我们计算这些预测值与这些年份的BSE Sensex收盘价的相互关系。我们发现在印度经济中，收盘价与汇率和货币供应量有很高的相关性。 摘要：The objective of this paper is to find the existence of a relationship between stock market prices and the fundamental macroeconomic indicators. We build a Vector Auto Regression (VAR) model comprising of nine major macroeconomic indicators (interest rate, inflation, exchange rate, money supply, gdp, fdi, trade-gdp ratio, oil prices, gold prices) and then try to forecast them for next 5 years. Finally we calculate cross-correlation of these forecasted values with the BSE Sensex closing price for each of those years. We find very high correlation of the closing price with exchange rate and money supply in the Indian economy.
2.cs.SD语音:
【1】 Chimpanzee voice prints? Insights from transfer learning experiments from human voices 标题：黑猩猩的声纹？来自人声的迁移学习实验的启示 链接：https://arxiv.org/abs/2112.08165
作者：Mael Leroux,Orestes Gutierrez Al-Khudhairy,Nicolas Perony,Simon W. Townsend 摘要：在动物界，个体声音差异无处不在。在人类中，这些差异遍及整个声乐曲目，并构成“声纹”。类人猿是我们现存的近亲，在特定的呼叫类型中拥有个体特征，但对其形成独特声纹的可能性研究甚少。这部分归因于从小型数据集中提取有意义特征的局限性。机器学习的进步突出了传统声学特征的另一种选择，即预先训练的学习提取器。在这里，我们提出了一种基于这些发展的方法：利用一个基于对10000多个人类声纹进行训练的深层神经网络的特征提取器，提供一个信息空间，在这个空间中我们可以识别黑猩猩的声纹。我们将我们的结果与通过使用传统声学特征获得的结果进行比较，并讨论我们的方法的益处以及我们的发现对于非人类动物“声纹”识别的意义。 摘要：Individual vocal differences are ubiquitous in the animal kingdom. In humans, these differences pervade the entire vocal repertoire and constitute a "voice print". Apes, our closest-living relatives, possess individual signatures within specific call types, but the potential for a unique voice print has been little investigated. This is partially attributed to the limitations associated with extracting meaningful features from small data sets. Advances in machine learning have highlighted an alternative to traditional acoustic features, namely pre-trained learnt extractors. Here, we present an approach building on these developments: leveraging a feature extractor based on a deep neural network trained on over 10,000 human voice prints to provide an informative space over which we identify chimpanzee voice prints. We compare our results with those obtained by using traditional acoustic features and discuss the benefits of our methodology and the significance of our findings for the identification of "voice prints" in non-human animals.
【2】 Speech frame implementation for speech analysis and recognition 标题：用于语音分析和识别的语音帧实现 链接：https://arxiv.org/abs/2112.08027
作者：A. A. Konev,V. S. Khlebnikov,A. Yu. Yakimuk 备注：7 pages, 27 tables 摘要：所创建的语音框架的显著特征是：能够考虑说话人的情绪状态，支持处理说话人语音形成区的疾病，以及存在大量语音信号的手动分割。此外，与大多数类比不同，该系统侧重于俄语语音材料。 摘要：Distinctive features of the created speech frame are: the ability to take into account the emotional state of the speaker, sup-port for working with diseases of the speech-forming tract of speakers and the presence of manual segmentation of a num-ber of speech signals. In addition, the system is focused on Russian-language speech material, unlike most analogs.
【3】 The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices 标题：多特征提取技术在伪装语音情感状态说话人识别中的应用 链接：https://arxiv.org/abs/2112.07940
作者：Noor Ahmad Al Hindawi,Ismail Shahin,Ali Bou Nassif 备注：5 pages, 1 figure, accepted in the 14th International Conference on Developments in eSystems Engineering, 7-10 December, 2021 摘要：由于人工智能技术的进步，说话人识别（SI）技术带来了巨大的发展方向，目前已广泛应用于各个领域。特征提取是SI最重要的组成部分之一，它对SI过程和性能有重大影响。因此，大量的特征提取策略被深入研究、对比和分析。本文利用五种不同的特征提取方法对情感环境下变相语音中的说话人进行识别。为了显著评估这项工作，使用了三种效果：高音、低音和电子语音转换（EVC）。实验结果表明，串联的Mel倒谱系数（mfcc）、MFCCsδ和MFCCsδ是最好的特征提取方法。 摘要：Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numerous feature extraction strategies are thoroughly investigated, contrasted, and analyzed. This article exploits five distinct feature extraction methods for speaker identification in disguised voices under emotional environments. To evaluate this work significantly, three effects are used: high-pitched, low-pitched, and Electronic Voice Conversion (EVC). Experimental results reported that the concatenated Mel-Frequency Cepstral Coefficients (MFCCs), MFCCs-delta, and MFCCs-delta-delta is the best feature extraction method.
【4】 Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data 标题：基于查询学习的弱标签数据零发音源分离 链接：https://arxiv.org/abs/2112.07891
作者：Ke Chen,Xingjian Du,Bilei Zhu,Zejun Ma,Taylor Berg-kirkpatrick,Shlomo Dubnov 备注：9 pages, 3 figures, 5 tables, preprint version for Association for the Advancement of Artificial Intelligence Conference, AAAI 2022 摘要：将音频分离为不同声源的深度学习技术面临若干挑战。标准体系结构要求针对不同类型的音频源训练不同的模型。尽管一些通用分离器采用单一模型来定位多个源，但它们很难推广到不可见源。在本文中，我们提出了一个三分量管道来从一个大型但标记较弱的数据集：AudioSet中训练通用音频源分离器。首先，我们提出了一个基于Transformer的声音事件检测系统，用于处理弱标记的训练数据。其次，我们设计了一个基于查询的音频分离模型，该模型利用这些数据进行模型训练。第三，我们设计了一个潜在的嵌入处理器来对指定音频目标进行分离的查询进行编码，从而实现Zero-Shot泛化。我们的方法使用单一模型来分离多种声音类型的源，并且仅依赖弱标记数据进行训练。此外，建议的音频分离器可用于Zero-Shot设置，学习分离训练中从未见过的音频源类型。为了评估分离性能，我们在MUSDB18上测试了我们的模型，同时在不相交的音频集上进行了训练。我们通过对训练中保留的音频源类型进行另一个实验，进一步验证了零炮性能。在这两种情况下，该模型的源失真比（SDR）性能与当前监督模型相当。 摘要：Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal separators employ a single model to target multiple sources, they have difficulty generalizing to unseen sources. In this paper, we propose a three-component pipeline to train a universal audio source separator from a large, but weakly-labeled dataset: AudioSet. First, we propose a transformer-based sound event detection system for processing weakly-labeled training data. Second, we devise a query-based audio separation model that leverages this data for model training. Third, we design a latent embedding processor to encode queries that specify audio targets for separation, allowing for zero-shot generalization. Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training. In addition, the proposed audio separator can be used in a zero-shot setting, learning to separate types of audio sources that were never seen in training. To evaluate the separation performance, we test our model on MUSDB18, while training on the disjoint AudioSet. We further verify the zero-shot performance by conducting another experiment on audio source types that are held-out from training. The model achieves comparable Source-to-Distortion Ratio (SDR) performance to current supervised models in both cases.
【5】 A literature review on COVID-19 disease diagnosis from respiratory sound data 标题：呼吸音诊断冠状病毒病的文献综述 链接：https://arxiv.org/abs/2112.07670
作者：Kranthi Kumar Lella,Alphonse PJA 备注：None 摘要：世界卫生组织（WHO）宣布了一个COVID-19是2020年3月全球流感大流行。它最初于2019年12月在中国启动，在过去几个月里影响了越来越多的国家。在这种特殊情况下，许多技术、方法和基于人工智能的分类算法成为人们关注的焦点，以应对这种情况并降低全球健康危机的发生率。新冠病毒-19的主要症状是体温过高、咳嗽、感冒、呼吸短促，以及嗅觉丧失和胸闷。数字世界日益发展，在此背景下，数字听诊器可以读取所有这些症状并诊断呼吸道疾病。2019冠状病毒疾病的研究进展主要集中在文献综述中，通过分析呼吸音参数，分析了SARS—COV-2是如何从COXVID-19型疾病的诊断中传播的。我们希望这一审查将为临床科学家和研究者社区提供一个主动性，以开启与CVID-19的集体战斗中的开放获取、可扩展和可访问的工作。 摘要：The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day, in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.
3.eess.AS音频处理:
【1】 RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies 标题：RawNeXt：具有深层聚集和扩展动态缩放策略的可变时长发音的说话人确认系统 链接：https://arxiv.org/abs/2112.07935
作者：Ju-ho Kim,Hye-jin Shim,Jungwoo Heo,Ha-Jin Yu 备注：5 pages, 2 figures, 4 tables, submitted to 2022 ICASSP as a conference paper 摘要：尽管使用深度神经网络在说话人确认方面取得了令人满意的性能，但可变时长的话语仍然是一个挑战，威胁着系统的鲁棒性。针对这个问题,，我们提出了一种称为RawNeXt的说话人验证系统，该系统可以通过以下两个组件处理任意长度的输入原始波形：（1）深层聚合策略通过迭代和分层聚合不同时间尺度的特征和从块输出的频谱通道来增强说话人信息。（2） 扩展的动态缩放策略通过选择性地合并每个块中不同分辨率分支的激活，根据话语长度灵活地处理特征。由于这两个部分，我们提出的模型可以提取出丰富的时间谱信息的说话人嵌入，并对长度变化进行动态操作。在VoxCeleb1测试集上的实验结果表明，与最近提出的系统相比，RawNeXt实现了最先进的性能。我们的代码和经过训练的模型重量可在https://github.com/wngh1187/RawNeXt. 摘要：Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems. To deal with this issue, we propose a speaker verification system called RawNeXt that can handle input raw waveforms of arbitrary length by employing the following two components: (1) A deep layer aggregation strategy enhances speaker information by iteratively and hierarchically aggregating features of various time scales and spectral channels output from blocks. (2) An extended dynamic scaling policy flexibly processes features according to the length of the utterance by selectively merging the activations of different resolution branches in each block. Owing to these two components, our proposed model can extract speaker embeddings rich in time-spectral information and operate dynamically on length variations. Experimental results on the VoxCeleb1 test set consisting of various duration utterances demonstrate that RawNeXt achieves state-of-the-art performance compared to the recently proposed systems. Our code and trained model weights are available at https://github.com/wngh1187/RawNeXt.
【2】 Textless Speech-to-Speech Translation on Real Data 标题：基于真实数据的无文本语音到语音翻译 链接：https://arxiv.org/abs/2112.08352
作者：Ann Lee,Hongyu Gong,Paul-Ambroise Duquenne,Holger Schwenk,Peng-Jen Chen,Changhan Wang,Sravya Popuri,Juan Pino,Jiatao Gu,Wei-Ning Hsu 摘要：我们提出了一个无文本语音转换（S2ST）系统，该系统可以将语音从一种语言转换为另一种语言，并且不需要任何文本数据。与现有文献中的工作不同，我们解决了多说话人目标语音建模的挑战，并使用真实世界的S2ST数据对系统进行训练。我们的方法的关键是一种基于自我监督单元的语音规范化技术，它使用来自多个说话人和一个参考说话人的成对音频对预先训练的语音编码器进行微调，以减少由于口音引起的变化，同时保留词汇内容。在语音标准化的配对数据只有10分钟的情况下，与在非标准化语音目标上训练的基线相比，在vp~S2ST数据集上训练S2ST模型时，我们平均获得3.2 BLEU增益。我们还加入了自动挖掘的S2ST数据，并显示了额外的2.0 BLEU增益。据我们所知，我们是第一个建立无文本S2ST技术的人，该技术可以使用真实世界的数据进行训练，并适用于多种语言对。 摘要：We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data. The key to our approach is a self-supervised unit-based speech normalization technique, which finetunes a pre-trained speech encoder with paired audios from multiple speakers and a single reference speaker to reduce the variations due to accents, while preserving the lexical content. With only 10 minutes of paired data for speech normalization, we obtain on average 3.2 BLEU gain when training the S2ST model on the vp~S2ST dataset, compared to a baseline trained on un-normalized speech target. We also incorporate automatically mined S2ST data and show an additional 2.0 BLEU gain. To our knowledge, we are the first to establish a textless S2ST technique that can be trained with real-world data and works for multiple language pairs.
【3】 Chimpanzee voice prints? Insights from transfer learning experiments from human voices 标题：黑猩猩的声纹？来自人声的迁移学习实验的启示 链接：https://arxiv.org/abs/2112.08165
作者：Mael Leroux,Orestes Gutierrez Al-Khudhairy,Nicolas Perony,Simon W. Townsend 摘要：在动物界，个体声音差异无处不在。在人类中，这些差异遍及整个声乐曲目，并构成“声纹”。类人猿是我们现存的近亲，在特定的呼叫类型中拥有个体特征，但对其形成独特声纹的可能性研究甚少。这部分归因于从小型数据集中提取有意义特征的局限性。机器学习的进步突出了传统声学特征的另一种选择，即预先训练的学习提取器。在这里，我们提出了一种基于这些发展的方法：利用一个基于对10000多个人类声纹进行训练的深层神经网络的特征提取器，提供一个信息空间，在这个空间中我们可以识别黑猩猩的声纹。我们将我们的结果与通过使用传统声学特征获得的结果进行比较，并讨论我们的方法的益处以及我们的发现对于非人类动物“声纹”识别的意义。 摘要：Individual vocal differences are ubiquitous in the animal kingdom. In humans, these differences pervade the entire vocal repertoire and constitute a "voice print". Apes, our closest-living relatives, possess individual signatures within specific call types, but the potential for a unique voice print has been little investigated. This is partially attributed to the limitations associated with extracting meaningful features from small data sets. Advances in machine learning have highlighted an alternative to traditional acoustic features, namely pre-trained learnt extractors. Here, we present an approach building on these developments: leveraging a feature extractor based on a deep neural network trained on over 10,000 human voice prints to provide an informative space over which we identify chimpanzee voice prints. We compare our results with those obtained by using traditional acoustic features and discuss the benefits of our methodology and the significance of our findings for the identification of "voice prints" in non-human animals.
【4】 Speech frame implementation for speech analysis and recognition 标题：用于语音分析和识别的语音帧实现 链接：https://arxiv.org/abs/2112.08027
作者：A. A. Konev,V. S. Khlebnikov,A. Yu. Yakimuk 备注：7 pages, 27 tables 摘要：所创建的语音框架的显著特征是：能够考虑说话人的情绪状态，支持处理说话人语音形成区的疾病，以及存在大量语音信号的手动分割。此外，与大多数类比不同，该系统侧重于俄语语音材料。 摘要：Distinctive features of the created speech frame are: the ability to take into account the emotional state of the speaker, sup-port for working with diseases of the speech-forming tract of speakers and the presence of manual segmentation of a num-ber of speech signals. In addition, the system is focused on Russian-language speech material, unlike most analogs.
【5】 The exploitation of Multiple Feature Extraction Techniques for Speaker Identification in Emotional States under Disguised Voices 标题：多特征提取技术在伪装语音情感状态说话人识别中的应用 链接：https://arxiv.org/abs/2112.07940
作者：Noor Ahmad Al Hindawi,Ismail Shahin,Ali Bou Nassif 备注：5 pages, 1 figure, accepted in the 14th International Conference on Developments in eSystems Engineering, 7-10 December, 2021 摘要：由于人工智能技术的进步，说话人识别（SI）技术带来了巨大的发展方向，目前已广泛应用于各个领域。特征提取是SI最重要的组成部分之一，它对SI过程和性能有重大影响。因此，大量的特征提取策略被深入研究、对比和分析。本文利用五种不同的特征提取方法对情感环境下变相语音中的说话人进行识别。为了显著评估这项工作，使用了三种效果：高音、低音和电子语音转换（EVC）。实验结果表明，串联的Mel倒谱系数（mfcc）、MFCCsδ和MFCCsδ是最好的特征提取方法。 摘要：Due to improvements in artificial intelligence, speaker identification (SI) technologies have brought a great direction and are now widely used in a variety of sectors. One of the most important components of SI is feature extraction, which has a substantial impact on the SI process and performance. As a result, numerous feature extraction strategies are thoroughly investigated, contrasted, and analyzed. This article exploits five distinct feature extraction methods for speaker identification in disguised voices under emotional environments. To evaluate this work significantly, three effects are used: high-pitched, low-pitched, and Electronic Voice Conversion (EVC). Experimental results reported that the concatenated Mel-Frequency Cepstral Coefficients (MFCCs), MFCCs-delta, and MFCCs-delta-delta is the best feature extraction method.
【6】 Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data 标题：基于查询学习的弱标签数据零发音源分离 链接：https://arxiv.org/abs/2112.07891
作者：Ke Chen,Xingjian Du,Bilei Zhu,Zejun Ma,Taylor Berg-kirkpatrick,Shlomo Dubnov 备注：9 pages, 3 figures, 5 tables, preprint version for Association for the Advancement of Artificial Intelligence Conference, AAAI 2022 摘要：将音频分离为不同声源的深度学习技术面临若干挑战。标准体系结构要求针对不同类型的音频源训练不同的模型。尽管一些通用分离器采用单一模型来定位多个源，但它们很难推广到不可见源。在本文中，我们提出了一个三分量管道来从一个大型但标记较弱的数据集：AudioSet中训练通用音频源分离器。首先，我们提出了一个基于Transformer的声音事件检测系统，用于处理弱标记的训练数据。其次，我们设计了一个基于查询的音频分离模型，该模型利用这些数据进行模型训练。第三，我们设计了一个潜在的嵌入处理器来对指定音频目标进行分离的查询进行编码，从而实现Zero-Shot泛化。我们的方法使用单一模型来分离多种声音类型的源，并且仅依赖弱标记数据进行训练。此外，建议的音频分离器可用于Zero-Shot设置，学习分离训练中从未见过的音频源类型。为了评估分离性能，我们在MUSDB18上测试了我们的模型，同时在不相交的音频集上进行了训练。我们通过对训练中保留的音频源类型进行另一个实验，进一步验证了零炮性能。在这两种情况下，该模型的源失真比（SDR）性能与当前监督模型相当。 摘要：Deep learning techniques for separating audio into different sound sources face several challenges. Standard architectures require training separate models for different types of audio sources. Although some universal separators employ a single model to target multiple sources, they have difficulty generalizing to unseen sources. In this paper, we propose a three-component pipeline to train a universal audio source separator from a large, but weakly-labeled dataset: AudioSet. First, we propose a transformer-based sound event detection system for processing weakly-labeled training data. Second, we devise a query-based audio separation model that leverages this data for model training. Third, we design a latent embedding processor to encode queries that specify audio targets for separation, allowing for zero-shot generalization. Our approach uses a single model for source separation of multiple sound types, and relies solely on weakly-labeled data for training. In addition, the proposed audio separator can be used in a zero-shot setting, learning to separate types of audio sources that were never seen in training. To evaluate the separation performance, we test our model on MUSDB18, while training on the disjoint AudioSet. We further verify the zero-shot performance by conducting another experiment on audio source types that are held-out from training. The model achieves comparable Source-to-Distortion Ratio (SDR) performance to current supervised models in both cases.
【7】 A literature review on COVID-19 disease diagnosis from respiratory sound data 标题：呼吸音诊断冠状病毒病的文献综述 链接：https://arxiv.org/abs/2112.07670
作者：Kranthi Kumar Lella,Alphonse PJA 备注：None 摘要：世界卫生组织宣布2019冠状病毒疾病是2020年3月全球流行。它最初于2019年12月在中国启动，在过去几个月里影响了越来越多的国家。在这种特殊情况下，许多技术、方法和基于人工智能的分类算法成为人们关注的焦点，以应对这种情况并降低全球健康危机的发生率。新冠病毒-19的主要症状是体温过高、咳嗽、感冒、呼吸短促，以及嗅觉丧失和胸闷。数字世界日益发展，在此背景下，数字听诊器可以读取所有这些症状并诊断呼吸道疾病。在这项研究中，我们主要集中在文献回顾SARS COV-2是如何传播和深入分析COVID-19疾病的诊断从人类呼吸的声音，如咳嗽，声音，和呼吸，通过分析呼吸声音参数。我们希望这一2019冠状病毒疾病的临床研究和研究人员能够为CVID-19的集体战斗提供开放、可扩展和可访问的工作。 摘要：The World Health Organization (WHO) has announced a COVID-19 was a global pandemic in March 2020. It was initially started in china in the year 2019 December and affected an expanding number of nations in various countries in the last few months. In this particular situation, many techniques, methods, and AI-based classification algorithms are put in the spotlight in reacting to fight against it and reduce the rate of such a global health crisis. COVID-19's main signs are heavy temperature, different cough, cold, breathing shortness, and a combination of loss of sense of smell and chest tightness. The digital world is growing day by day, in this context digital stethoscope can read all of these symptoms and diagnose respiratory disease. In this study, we majorly focus on literature reviews of how SARS-CoV-2 is spreading and in-depth analysis of the diagnosis of COVID-19 disease from human respiratory sounds like cough, voice, and breath by analyzing the respiratory sound parameters. We hope this review will provide an initiative for the clinical scientists and researcher's community to initiate open access, scalable, and accessible work in the collective battle against COVID-19.
