scholarly journals Development of Machine Learning for Asthmatic and Healthy Voluntary Cough Sounds: A Proof of Concept Study

2019 ◽  
Vol 9 (14) ◽  
pp. 2833 ◽  
Author(s):  
Hwan Ing Hee ◽  
BT Balamurali ◽  
Arivazhagan Karunakaran ◽  
Dorien Herremans ◽  
Onn Hoe Teoh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Demographic Data ◽  
Predictive Performance ◽  
Gaussian Mixture ◽  
Classification Model ◽  
Healthy Children ◽  
Mel Frequency Cepstral Coefficients ◽  
Voluntary Cough ◽  
Audio Features ◽  
Cepstral Coefficients

(1) Background: Cough is a major presentation in childhood asthma. Here, we aim to develop a machine-learning based cough sound classifier for asthmatic and healthy children. (2) Methods: Children less than 16 years old were randomly recruited in a Children’s Hospital, from February 2017 to April 2018, and were divided into 2 cohorts—healthy children and children with acute asthma presenting with cough. Children with other concurrent respiratory conditions were excluded in the asthmatic cohort. Demographic data, duration of cough, and history of respiratory status were obtained. Children were instructed to produce voluntary cough sounds. These clinically labeled cough sounds were randomly divided into training and testing sets. Audio features such as Mel-Frequency Cepstral Coefficients and Constant-Q Cepstral Coefficients were extracted. Using a training set, a classification model was developed with Gaussian Mixture Model–Universal Background Model (GMM-UBM). Its predictive performance was tested using the test set against the physicians’ labels. (3) Results: Asthmatic cough sounds from 89 children (totaling 1192 cough sounds) and healthy coughs from 89 children (totaling 1140 cough sounds) were analyzed. The sensitivity and specificity of the audio-based classification model was 82.81% and 84.76%, respectively, when differentiating coughs from asthmatic children versus coughs from ‘healthy’ children. (4) Conclusion: Audio-based classification using machine learning is a potentially useful technique in assisting the differentiation of asthmatic cough sounds from healthy voluntary cough sounds in children.

scholarly journals Comparison of feature extraction and normalization methods for speaker recognition using grid-audiovisual database

2020 ◽  
Vol 18 (2) ◽  
pp. 782
Author(s):  
Musab T. S. Al-Kaltakchi ◽  
Haithem Abd Al-Raheem Taha ◽  
Mohanad Abd Shehab ◽  
Mohamed A.M. Abdullah
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Identification Accuracy ◽  
Identification System ◽  
Good Representation ◽  
Mel Frequency Cepstral Coefficients ◽  
Normalization Methods ◽  
Cepstral Coefficients

<p><span lang="EN-GB">In this paper, different feature extraction and feature normalization methods are investigated for speaker recognition. With a view to give a good representation of acoustic speech signals, Power Normalized Cepstral Coefficients (PNCCs) and Mel Frequency Cepstral Coefficients (MFCCs) are employed for feature extraction. Then, to mitigate the effect of linear channel, Cepstral Mean-Variance Normalization (CMVN) and feature warping are utilized. The current paper investigates Text-independent speaker identification system by using 16 coefficients from both the MFCCs and PNCCs features. Eight different speakers are selected from the GRID-Audiovisual database with two females and six males. The speakers are modeled using the coupling between the Universal Background Model and Gaussian Mixture Models (GMM-UBM) in order to get a fast scoring technique and better performance. The system shows 100% in terms of speaker identification accuracy. The results illustrated that PNCCs features have better performance compared to the MFCCs features to identify females compared to male speakers. Furthermore, feature wrapping reported better performance compared to the CMVN method. </span></p>

scholarly journals The impact of data-complexity and team characteristics on performance in the classification model

2022 ◽  
Vol 9 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Class Imbalance ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Classification Model ◽  
Data Complexity ◽  
High Performing ◽  
Machine Learning Model ◽  
The Impact

This article investigates the impact of data-complexity and team-specific characteristics on machine learning competition scores. Data from five real-world binary classification competitions hosted on Kaggle.com were analyzed. The data-complexity characteristics were measured in four aspects including standard measures, sparsity measures, class imbalance measures, and feature-based measures. The results showed that the higher the level of the data-complexity characteristics was, the lower the predictive ability of the machine learning model was as well. Our empirical evidence revealed that the imbalance ratio of the target variable was the most important factor and exhibited a nonlinear relationship with the model’s predictive abilities. The imbalance ratio adversely affected the predictive performance when it reached a certain level. However, mixed results were found for the impact of team-specific characteristics measured by team size, team expertise, and the number of submissions on team performance. For high-performing teams, these factors had no impact on team score.

scholarly journals Artificial Neural Network Based Amharic Language Speaker Recognition

2021 ◽  
Vol 12 (3) ◽  
pp. 5105-5116
Author(s):  
Gizachew Belayneh Gebre Et. al.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speaker Recognition ◽  
Gaussian Mixture ◽  
Fourth Generation ◽  
Mel Frequency Cepstral Coefficients ◽  
Authorized User ◽  
Artificial Neural ◽  
Cepstral Coefficients ◽  
High Level

In this artificial intelligence time, speaker recognition is the most useful biometric recognition technique. Security is a big issue that needs careful attention because of every activities have been becoming automated and internet based. For security purpose, unique features of authorized user are highly needed. Voice is one of the wonderful unique biometric features. So, developing speaker recognition based on scientific research is the most concerned issue. Nowadays, criminal activities are increasing day to day in different clever way. So, every country should have strengthen forensic investigation using such technologies. The study was done by inspiration of contextualizing this concept for our country. In this study, text-independent Amharic language speaker recognition model was developed using Mel-Frequency Cepstral Coefficients to extract features from preprocessed speech signals and Artificial Neural Network to model the feature vector obtained from the Mel-Frequency Cepstral Coefficients and to classify objects while testing. The researcher used 20 sampled speeches of 10 each speaker (total of 200 speech samples) for training and testing separately. By setting the number of hidden neurons to 15, 20, and 25, three different models have been developed and evaluated for accuracy. The fourth-generation high-level programming language and interactive environment MATLAB is used to conduct the overall study implementations. At the end, very promising findings have been obtained. The study achieved better performance than other related researches which used Vector Quantization and Gaussian Mixture Model modelling techniques. Implementable result could obtain for the future by increasing number of speakers and speech samples and including the four Amharic accents.

Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning System (Preprint)

2021 ◽  
Author(s):  
Nathan Chi ◽  
Peter Washington ◽  
Aaron Kline ◽  
Arman Husic ◽  
Cathy Hou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neurodevelopmental Disorder ◽  
Model Performance ◽  
Autism Spectrum ◽  
Learning System ◽  
Learning Approaches ◽  
Mel Frequency Cepstral Coefficients ◽  
Audio Features ◽  
Specialized Equipment ◽  
Child Speech

BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence has tripled, with 1 in 54 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process which requires the work of trained physicians, significant attention has been given to developing systems that automatically diagnose and screen for autism. OBJECTIVE Prosody abnormalities are among the most clear signs of autism, with affected children displaying speech idiosyncrasies (including echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns). In this work, we present a suite of machine learning approaches to detect autism in self-recorded speech audio captured from autistic and neurotypical (NT) children in home environments. METHODS We consider three methods to detect autism in child speech: first, Random Forests trained on extracted audio features (including Mel-frequency cepstral coefficients); second, convolutional neural networks (CNNs) trained on spectrograms; and third, fine-tuned wav2vec 2.0—a state-of-the-art Transformer-based speech recognition model. We train our classifiers on our novel dataset of cellphone-recorded child speech audio curated from Stanford’s Guess What? mobile game, an app designed to crowdsource videos of autistic and neurotypical children in a natural home environment. RESULTS The Random Forest classifier achieves 70% accuracy, the fine-tuned wav2vec 2.0 model achieves 77% accuracy, and the CNN achieves 79% accuracy when classifying children’s audio as either ASD or NT. We use five-fold cross-validation to evaluate model performance. CONCLUSIONS Our models were able to predict autism status when training on a varied selection of home audio clips with inconsistent recording qualities, which may be more generalizable to real world conditions. The results demonstrate that machine learning methods offer promise in detecting autism automatically from speech without specialized equipment.

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