A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough

The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.

An Advanced Deep Learning Medical Image Recognition and Diagnosis of Respiratory System Viruses

Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.

SRC: Superior Robustness of COVID-19 Detection from Noisy Cough Data Using GFCC

This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients (GFCC) for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network (DHO-ANN). The noisy crowdsourced cough datasets were collected from the public domain. This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora. The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures, F1 score, confusion matrix, specificity, and sensitivity parameters. Besides, it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset, COUGHVID. Moreover, the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5% compared to the existing methods. © 2023 CRL Publishing. All rights reserved.

Automatic illness prediction system through speech.

Due to the COVID-19 epidemic and the curfew caused by it, many people have sought to find an ADPS on the internet in the last few years. This hints to a new age of medical treatment, all the more so if the number of internet users continues to expand. As a result, automatic illness prediction online applications have attracted the interest of a large number of researchers worldwide. This work aims to develop and implement an automated illness prediction system based on speech. The system will be designed to forecast the sort of ailment a patient is suffering from based on his voice, but this was not feasible during the trial, therefore the diseases were divided into three categories (painful, light pain and psychological pain), and then the diagnose process were implemented accordingly. The medical dataset named "speech, transcription, and intent" served as the baseline for this study. The smoothness, MFCC, and SCV properties were used in this work, which demonstrated their high representation to human being medical situations. The noise reduction forward-backward filter was used to eliminate noise from wave files captured online in order to account for the high level of noise seen in the deployed dataset. For this study, a hybrid feature selection method was created and built that combined the output of a genetic algorithm (GA) with the inputs of a NN algorithm. Classification was performed using SVM, neural network, and GMM. The greatest results obtained were 94.55% illness classification accuracy in terms of SVM. The results showed that diagnosing illness through speech is a difficult process, especially when diagnosing each type of illness separately, but when grouping the different illness types into groups, depending on the amount of pain and the psychological situation of the patient, the results were much higher.

Sound Identification using MFCC with Machine Learning

This article presents an application of sound identification using machine learning techniques. Identification for access control system such as an entering-exiting turnstile in the building gate is still required for people's working lives, in general. However, under COVID-19 pandemic, a new norm or New-Normal has emerged to reduce and prevent the spread of the COVID-19 virus. Sound identification system is considered as a system of identification/authentication without any direct contact between the people and the system equipment. Therefore, in this work, a sound identification system is studied and developed. To analyze and feature-extract the sound from a pre-processed human voice, MFCC (Mel Frequency Cepstral Coefficient) technique is adopted. For identification process, the feature vector obtained from MFCC is sent to 3 different popular machine learning techniques.;namely, CNN (Convolutional Neural Network), GMM (Gaussian Mixture Models), and SVM (Support Vector Machine). This results in sound authentication with true positive accuracy of 87.90%, 52.98%, and 41.37%, respectively, and true negative accuracy of 52.98%, 35.12%, and 90.48%, respectively. The best true positive and true negative accuracies are from CNN and SVM, respectively. The results can be further applied in sound identification system. © 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).

Artificial Intelligence Based CoughMonitoring System For Covid-19

Infection with the SARS-CoV-2 virus results in Covid 19, an infectious illness. Most persons who get Coronavirus will only experience mild to moderate symptoms and will get better without any special care. Some people get very sick and need medical attention. The rising mortality toll from COVID-19 underscores the importance of developing methods for early detection of the disease, which might aid in containing the epidemic and facilitating the creation of tailored mitigation strategies. Current research in chaotic dynamics indicates that coughs and other vocal sounds include lung health data that can be used for symptomatic reasons. Mel frequencies Cepstral Coefficients (MFCC) are applied to cough samples, and then the audio data from coughs is fed into a GridsearchCV model using a KNN-based classification method. Our model was developed using 217 samples from training data and 55 from testing data. Cough tests conducted on both males and females are included in the dataset. An evaluation found that the model had an accuracy of 83.3%. © 2022 IEEE.

Multiclass Categorisation of Respiratory Sound Signals Using Neural Network

Respiratory diseases have seriously impacted human life in the last couple of years;as Covid 19 arrived, many lost their beloved ones. Since respiratory diseases directly attack the patient's lungs, it is becoming risky day by day for human life and doctors because a confined number of resources are available in hospitals to detect these respiratory diseases, and detection of these diseases is a difficult job to the doctors. Therefore early-stage diagnosis can help the doctor in saving human lives. Researchers are continuously trying to help doctors by designing efficient and more accurate tools for detecting different types of respiratory diseases. This paper uses a convolution-based deep learning model to classify these respiratory diseases using patient respiratory sound signals with Mel frequency cepstral coefficients (MFFCs) as a feature vector. In this paper, we have tried to keep our neural network model as simple as possible with less trainable parameters and good classification accuracy. The model performance is measured in terms of sensitivity, specificity, average score, and harmonic score. © 2022 IEEE.

Advanced Medical Images Recognition and Diagnosis of Respiratory System Viruses

Detection of respiratory viruses is a perplexing task which regularly requires saving time by taking a quick look at clinical images of patients ceaselessly. Hence, there's a need to propose and develop a model to predict the respiratory viruses (COVID-19) cases at the earliest possible to control the spread of disease. Deep learning makes it possible to find out that Covid-19 can be detected in an efficient way using its classification tools such as CNN (Convolutional Neural Network). MFCC (Mel Frequency Cepstral Coefficients) is a very common and efficient technique for signal processing. In this research, a MFCC - CNN learning model to hasten the prediction process is proposed that assist the medical professionals. MFCC is used for extracting the image's features concerning existence of COVID-19 or not. Classification is performed by using convolutional neural network. This makes the time-consuming process easier and faster with more accurate results for radiologists and this reduces the spread of virus and save lives. Experimental results shows that using CT image converted to Mel-frequency cepstral coefficient spectrogram images as input to a CNN can achieve a high accuracy results;with classification of validation data scoring an accuracy of 99.08% correct classification of COVID and NON COVID labeled images. Hence, it can be used practically for detection of COVID-19 from CT images. The work here provides a proof of concept that high accuracy can be achieved with a moderate dataset, which can have a significant impact in this area. © 2022 Croatian Society MIPRO.

Covid Cough Classification using KNN Classification Algorithm

Covid 19 is an infectious disease that is caused by infection due to SARS-CoV-2 virus. The vast majority of people infected with Corona virus will encounter mild to moderate symptoms and recover without any special treatment. In some case, some people become seriously ill and require clinical consideration. Because of the increase in number of death due to COVID-19, an techniques for the early discovery of the illness is very much needed that might assist with restricting its spread just as help in the development of targeted surrounding solutions. Coughs and other vocal sounds contain pulmonary health data that can be utilized for symptomatic purposes, and ongoing examinations in chaotic dynamics have shows a nonlinear phenomenon exists in vocal signs. Cough samples are transformed with Mel frequency Cepstral Coefficients (MFCC) and the cough audio data is fitted into a GridsearchCV model with KNN based classification algorithm. The number of training data for used for training our model is 217 and remaining 55 data were used for testing the model. The dataset contains the cough tests from both male and female. When evaluated the model could get a precision of 83.3%. © 2022 IEEE.

Accent Classification of Native and Non-Native Children using Harmonic Pitch

To combat the Covid-19 outbreak, the education system shifted away from the classroom to distinct e-learning on digital platforms, which made effective use of voice-based recognition systems, especially for preliterate children. Children’s speech recognition systems face multiple challenges owing to their immature vocal tracts, and they demand more intelligence due to the fact that children with diverse accents utter words differently. Accent refers to a unique style of pronouncing a language, particularly one associated with a specific nation, place, or socio-economic background. This paper aims to extract reliable acoustic and prosodic speech cues of accent for classification of native and non-native preschool children using harmonic pitch estimation along with Mel Frequency Cepstral Coefficients (MFCCs) to train the k-Nearest Neighbour (k-NN) classifier. The experimental results reveal that the proposed robust model outperforms various feature extractors in accent classification of native and non-native children in terms of accuracy & F-Measure and more discriminate against noisy environments. © 2022 IEEE.

Human-Robot Interaction in Bengali language for Healthcare Automation integrated with Speaker Recognition and Artificial Conversational Entity

The research study presents an architecture of HumanRobot Interaction (HRI) based Artificial Conversational Entity integrated with speaker recognition ability to avail modern healthcare services. Due to the Covid-19 pandemic, the situation has become troublesome for health workers and patients to visit hospitals because of the high risk of virus dissemination. To minimize the mass congestion, our developed architecture would be an appropriate, cost-effective solution that automates the reception system by enabling AI-based HRI and providing fast and advanced healthcare services in the context of Bangladesh. The architecture consists of two significant subsections: Speaker Recognition and Artificial Conversational Entities having Automatic Speech Recognition in Bengali, Interactive Agent, and Text-to-Speech-synthesis. We used MFCC features as the linguistic parameters and the GMM statistical model to adapt each speaker's voice and estimation and maximization algorithm to identify the speaker's identity. The developed speaker recognition module performed significantly with 94.38% average accuracy in noisy environments and 96.27% average accuracy in studio quality environments and achieved a word error rate (WER) of 42.15% from RNN based Deep Speech 2 model for Bangla Automatic Speech Recognition (ASR). Besides, Artificial Conversational Entity performs with an average accuracy of 98.58% in a small-scale real-time environment. © 2021 IEEE.

Emotion Mining from Speech in the Covid-19 Era: An Exploratory Study

Speech is the most effective form of communication because it is not limited to just the linguistic components but carries the speaker's emotions laced within the components like tone of voice and cues like cries and sighs. This paper aims at studying the research done in the past and applying it to the Covid-19 era.The pandemic is of a great magnitude, affecting every aspect of life including emotions. This time period requires research in determining the most dominant emotions in conversations, to serve as a reference for future research and as a contrast to the research done in the past. Previous papers have identified emotions like happiness, anger, fear and sadness using feature extraction algorithms like MFCC (Mel Frequency Cepstral Coefficients and numerous classification algorithms like GMM (Gaussian Mixture Model), SVM (Support Vector Machine), KNN (K-Nearest-neighbor) and HMM (Hidden Markov Model). Some research has pointed towards ASR (Automatic Speech Recognition), N-Grams and vector space modeling. This paper aims at recognizing the most suitable algorithms for determining the pandemic specific emotions in speech. © 2021 IEEE.

Sound-Based Respiratory Disease Recognition with Machine Learning

Due to the effects of respiratory diseases, a large number of people die every year. Last year, the new coronavirus COVID-19 swept the world even more, causing the huge loss of personnel and economic decline around the world. To fight against this huge epidemic, relying on hospitals only to detect the such large number of people is obviously inefficient. Therefore, this paper proposes to relieve the pressure of medical workers through sound detection, in a framework of combing the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Networks (CNN). To compare different voice types and disease types, we selected three open data sets, namely, ICBHI, Coswara and Verify. Then we compared CNN with the Multi-layer Perceptron, Random Forest and XGBoost models. Finally, it is concluded that CNN has the highest accuracy rate on the three data sets. © 2021, Springer Nature Singapore Pte Ltd.

Forecasting the potential influence of Covid-19 using Data Science and Analytics

The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts of the number of substantiations and deaths. LSTM, GRUs, and Prophet are the major models created and tested for the solution. An LSTM model is a type of Recurrent Neural Network that is used to forecast datasets with increasingly changing patterns. Gated recurrent units only has two gateways: reboot and update. The prophet is best suited for forecasting assignments involving observation swith at least a year of history. The various models discussed above were used to the covid-19 data set to forecast the number of positive cases, active cases, and deaths associated with covid-19. We trained the model using data from April and May 2021 to demonstrate a comparison between the observed and expected number of positive events. To assume the future happing of COVID-19 by applying models which are in use, so that we will be able to calculate the impact of the disease's potential spread throughout the human being, preparing our selves to make proper planning and idea to prevent further transmission and equip health systems to manage the disease properly and battle the worldwide pandemic. © 2021 IEEE.

Performance Evaluation of an Intelligent Lung Sound Classifier Based on an Enhanced MFCC Model

The rate at which technology grew in the past years is unbelievably fast and astounding. However, chronic illnesses like respiratory diseases remains a common and widely experienced problem globally. The emergence of infectious respiratory health issues such as the coronavirus (COVID-19) had only made this enigma more harmful, causing an increase in the number of death due to respiratory illnesses. Hence, the development of modern and accurate methods to improve medical diagnosis is one of the simple step's humans can perform to overcome such problems. In this study, the researchers proposed an enhanced model for lung sound classification using Mel Frequency Cepstral Coefficient (MFCC). The design will classify four different lung sounds, with data input taken and classified one at a time. The goal of which is to augment human intelligence and not to replace the existing lung sound classification methods. The pre-recorded lung sounds were characterized, and the researcher proposed four enhanced MFCC models with three varying designs. The data collected from feature extraction and data mining were evaluated by the machine learning algorithms Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Measures like sensitivity, specificity, and accuracy were tested to determine which model was superior. Results showed that in terms of performance metrics, KNN performed better than SVM in classifying lung sounds. Tested in three designs where the pre-emphasis was removed, and the original 44.1kHz data resampled. Model 3 using KNN sampled at a frequency of 12000Hz has reached an average accuracy of 96.92% and a blind-data accuracy of 93.33%. A specificity of 97.94% and a sensitivity of 93.83%, achieving a performance that is comparable with existing studies on lung sound classification. © 2021 IEEE.

Cough Detection Using Selected Informative Features from Audio Signals

Cough is a common symptom of respiratory and lung diseases. Cough detection is important to prevent, assess and control epidemic, such as COVID-19. This paper proposes a model to detect cough events from cough audio signals. The models are trained by the dataset combined ESC-50 dataset with self-recorded cough recordings. The test dataset contains inpatient cough recordings collected from inpatients of the respiratory disease department in Ruijin Hospital. We totally build 15 cough detection models based on different feature numbers selected by Random Frog, Uninformative Variable Elimination (UVE), and Variable influence on projection (VIP) algorithms respectively. The optimal model is based on 20 features selected from Mel Frequency Cepstral Coefficients (MFCC) features by UVE algorithm and classified with Support Vector Machine (SVM) linear two-class classifier. The best cough detection model realizes the accuracy, recall, precision and F1-score with 94.9%, 97.1%, 93.1% and 0.95 respectively. Its excellent performance with fewer dimensionality of the feature vector shows the potential of being applied to mobile devices, such as smartphones, thus making cough detection remote and non-contact. © 2021 IEEE.