Detection of Common Cold from Speech Signals using Deep Neural Network

Deb, Suman; Warule, Pankaj; Nair, Amrita; Sultan, Haider; Dash, Rahul K; Krajewski, Jarek

doi:10.1007/s00034-022-02189-y

Cited by 14 publications

(4 citation statements)

References 37 publications

(17 reference statements)

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“…When using automatic classifications on real-life data, it is helpful to analyze the confusion between the categories to deal with the intrinsic ambiguities of emotions. In this line, studies on recognizing the user's state of health, like having a cold [50], have been conducted, while Borna et al (2023) review pain detection based on automatic voice analysis [51]. Other research studies the emotional and physiological reactions of users when interacting with an artificial agent.…”

Section: Personalized Communication Using Smart Conversational Agentsmentioning

confidence: 99%

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

Lehmann,

Granrath,

Browne

et al. 2024

Sustainability

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One of the central social challenges of the 21st century is society’s aging. AI provides numerous possibilities for meeting this challenge. In this context, the concept of digital twins, based on Cyber-Physical Systems, offers an exciting prospect. The e-VITA project, in which a virtual coaching system for elderly people is being created, allows the same to be assessed as a model for development. This white paper collects and presents relevant findings from research areas around digital twin technologies. Furthermore, we address ethical issues. This paper shows that the concept of digital twins can be usefully applied to older adults. However, it also shows that the required technologies must be further developed and that ethical issues must be discussed in an appropriate framework. Finally, the paper explains how the e-VITA project could pave the way towards developing a Digital Twin for Ageing.

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Section: Personalized Communication Using Smart Conversational Agentsmentioning

confidence: 99%

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

Lehmann,

Granrath,

Browne

et al. 2024

Sustainability

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“…The MFCC is considered to be the most important characteristic of all aspects of speech signal processing, including speech pathology and speech emotion detection. The MFCCs is extracted using the principles underlying human sound perception [14][15][16][17]. The procedures involved in obtaining the MFCC are explained in Fig.…”

Section: Mel Frequency Cepstral Coefficientmentioning

confidence: 99%

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

Nayak

Darji

Shah

2023

SIViP

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The Covid-19 pandemic is one of the most significant global health concerns that have emerged in this decade. Intelligent healthcare technology and techniques based on speech signal and artificial intelligence make it feasible to provide a faster and more efficient timely detection of Covid-19. The main objective of our study is to design speech signal-based noninvasive, low-cost, remote diagnosis of Covid-19. In this study, we have developed system to detect Covid-19 from speech signal using Mel frequency magnitude coefficients (MFMC) and machine learning techniques. In order to capture higher-order spectral features, the spectrum is divided into a larger number of subbands with narrower bandwidths as MFMC, which leads to better frequency resolution and less overall noise. As a consequence of an improvement in frequency resolution as well as a decrease in the quantity of noise that is included with the extraction of MFMC, the higher-order MFMCs are able to identify Covid-19 from speech signals with an increased level of accuracy. The procedures for machine learning are often less complicated than those for deep learning, and they may commonly be carried out on regular computers. However, deep learning systems need extensive computing power and data storage. Twelve, twenty-four, thirty, and forty spectral coefficients are obtained using MFMC in our study, and from these coefficients, performance is accessed using machine learning classifiers, such as random forests and K -nearest neighbor (KNN); however, KNN has performed better than the other model with having AUC score of 0.80.

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“…Many medical conditions can be accurately identified using computer-aided voice pathology classification tools and deep learning techniques. For example, a recent study (Deb et al, 2022)…”

Section: Studies Of Human Sounds For Medical Screeningmentioning

confidence: 99%

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

Hoang,

Nguyen,

Nguyen

et al. 2023

PHMAP_CONF

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As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experimentsshow that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.

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Detection of Common Cold from Speech Signals using Deep Neural Network

Cited by 14 publications

References 37 publications

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

Digital Twins for Supporting Ageing Well: Approaches in Current Research and Innovation in Europe and Japan

Machine learning approach for detecting Covid-19 from speech signal using Mel frequency magnitude coefficient

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

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