scholarly journals A Suggestive Diagnostic Technique For Early Identification Of Acyanotic Heart Disorders From Infant’s Cry

2013 ◽  
pp. 200-206
Author(s):  
Radhika Rani L ◽  
S. Chandra lingam ◽  
Anjaneyulu T ◽  
Satyanarayana K
Keyword(s):  
Fundamental Frequency ◽  
Linear Prediction ◽  
Vocal Tract ◽  
Early Stage ◽  
Heart Defects ◽  
Diagnostic Technique ◽  
Heart Disorder ◽  
Pitch Frequency ◽  
Chest X Ray ◽  
Heart Disorders

Congenital Heart Defects (CHD) are the critical heart disorders that can be observed at the birth stage of the infants. These are classified mainly into two, Cyanotic and Acyanotic. The present paper concentrates on the Acyanotic heart disorders. Acyanotic heart disorder cannot be observed on external checkup, whereas bluish skin is the indication for the infant affected with Cyanotic disorder. Acyanotic heart disorder can only be diagnosed using chest X-Ray, ECG, Echocardiogram, Cardiac Catheterization and MRI of the Heart. The present work aims at estimating the fundamental frequency (pitch) and the vocal tract resonant frequencies (formants) from the cry signal of the infants. The pitch frequency and formant frequencies are estimated using frequency domain (Cepstrum) and Linear Prediction Code (LPC) methods. The results show that the fundamental frequency of the cry signal was between 600Hz-800Hz for the infants with Acyanotic heart disorders. This fundamental frequency helps in identifying Acyanotic heart disorders at an early stage.

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

Contributions of Voice and Nonverbal Communication to Perceived Masculinity–Femininity for Cisgender and Transgender Communicators

2020 ◽  
Vol 63 (4) ◽  
pp. 931-947
Author(s):  
Teresa L. D. Hardy ◽  
Carol A. Boliek ◽  
Daniel Aalto ◽  
Justin Lewicke ◽  
Kristopher Wells ◽  
...  
Keyword(s):  
Fundamental Frequency ◽  
Sound Pressure Level ◽  
Sound Pressure ◽  
Vocal Tract ◽  
Presentation Mode ◽  
Pressure Level ◽  
Presentation Modes ◽  
Audiovisual Stimuli ◽  
Vocal Tract Resonance ◽  
Point Light

Purpose The purpose of this study was twofold: (a) to identify a set of communication-based predictors (including both acoustic and gestural variables) of masculinity–femininity ratings and (b) to explore differences in ratings between audio and audiovisual presentation modes for transgender and cisgender communicators. Method The voices and gestures of a group of cisgender men and women ( n = 10 of each) and transgender women ( n = 20) communicators were recorded while they recounted the story of a cartoon using acoustic and motion capture recording systems. A total of 17 acoustic and gestural variables were measured from these recordings. A group of observers ( n = 20) rated each communicator's masculinity–femininity based on 30- to 45-s samples of the cartoon description presented in three modes: audio, visual, and audio visual. Visual and audiovisual stimuli contained point light displays standardized for size. Ratings were made using a direct magnitude estimation scale without modulus. Communication-based predictors of masculinity–femininity ratings were identified using multiple regression, and analysis of variance was used to determine the effect of presentation mode on perceptual ratings. Results Fundamental frequency, average vowel formant, and sound pressure level were identified as significant predictors of masculinity–femininity ratings for these communicators. Communicators were rated significantly more feminine in the audio than the audiovisual mode and unreliably in the visual-only mode. Conclusions Both study purposes were met. Results support continued emphasis on fundamental frequency and vocal tract resonance in voice and communication modification training with transgender individuals and provide evidence for the potential benefit of modifying sound pressure level, especially when a masculine presentation is desired.

scholarly journals On the Speech Properties and Feature Extraction Methods in Speech Emotion Recognition

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1888
Author(s):  
Juraj Kacur ◽  
Boris Puterka ◽  
Jarmila Pavlovicova ◽  
Milos Oravec
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Filter Banks ◽  
Vocal Tract ◽  
Statistical Tests ◽  
Extraction Methods ◽  
Speech Emotion Recognition ◽  
Speech Characteristics ◽  
Evaluation Phase ◽  
Cepstral Features

Many speech emotion recognition systems have been designed using different features and classification methods. Still, there is a lack of knowledge and reasoning regarding the underlying speech characteristics and processing, i.e., how basic characteristics, methods, and settings affect the accuracy, to what extent, etc. This study is to extend physical perspective on speech emotion recognition by analyzing basic speech characteristics and modeling methods, e.g., time characteristics (segmentation, window types, and classification regions—lengths and overlaps), frequency ranges, frequency scales, processing of whole speech (spectrograms), vocal tract (filter banks, linear prediction coefficient (LPC) modeling), and excitation (inverse LPC filtering) signals, magnitude and phase manipulations, cepstral features, etc. In the evaluation phase the state-of-the-art classification method and rigorous statistical tests were applied, namely N-fold cross validation, paired t-test, rank, and Pearson correlations. The results revealed several settings in a 75% accuracy range (seven emotions). The most successful methods were based on vocal tract features using psychoacoustic filter banks covering the 0–8 kHz frequency range. Well scoring are also spectrograms carrying vocal tract and excitation information. It was found that even basic processing like pre-emphasis, segmentation, magnitude modifications, etc., can dramatically affect the results. Most findings are robust by exhibiting strong correlations across tested databases.

scholarly journals Imaging features and differential diagnoses of non-neoplastic diffuse mediastinal diseases

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Flavian Tabotta ◽  
Gilbert R. Ferretti ◽  
Helmut Prosch ◽  
Samia Boussouar ◽  
Anne-Laure Brun ◽  
...  
Keyword(s):  
Early Stage ◽  
Specific Treatment ◽  
Imaging Features ◽  
Mediastinal Diseases ◽  
Pictorial Review ◽  
Wide Range ◽  
Chest X Ray ◽  
Delayed Recognition ◽  
Patient’S History ◽  
Contiguous Spread

Abstract Acute or chronic non-neoplastic diffuse mediastinal diseases have multiple causes, degrees of severity, and a wide range of management. Some situations require emergency care while others do not need specific treatment. Although the diagnosis may be suspected on chest X-ray, it is mainly based on CT. A delayed recognition is not uncommonly observed. Some findings may prompt the radiologist to look for specific associated injuries or lesions. This pictorial review will successively describe the various non-neoplastic causes of diffuse mediastinal diseases with their typical findings and major differentials. First, pneumomediastinum that can be provoked by extra- or intra-thoracic triggers requires the knowledge of patient’s history or recent occurrences. Absence of any usual etiological factor should raise suspicion of cocaine inhalation in young individuals. Next, acute mediastinitis may be related to post-operative complications, esophageal perforation, or contiguous spread of odontogenic or retropharyngeal infections. The former diagnosis is not an easy task in the early stage, owing to the similarities of imaging findings with those of normal post-operative appearance during the first 2–3 weeks. Finally, fibrosing mediastinitis that is linked to an excessive fibrotic reaction in the mediastinum with variable compromise of mediastinal structures, in particular vascular and airway ones. Differential diagnosis includes tumoral and inflammatory infiltrations of the mediastinum.

Une nouvelle méthode de mesure de la fonction d'aire du conduit vocal : cas des voyelles

2005 ◽  
Vol 83 (7) ◽  
pp. 721-737
Author(s):  
H Teffahi ◽  
B Guerin ◽  
A Djeradi
Keyword(s):  
Measurement Method ◽  
Cross Correlation ◽  
Sound Production ◽  
Linear Prediction ◽  
Vocal Tract ◽  
Random Sequence ◽  
Speech Sound ◽  
Acoustic Properties ◽  
External Excitation ◽  
White Noise Excitation

Knowledge of vocal tract area functions is important for the understanding of phenomena occurring during speech production. We present here a new measurement method based on the external excitation of the vocal tract with a known pseudo-random sequence, where the area function is obtained by a linear prediction analysis applied to the cross-correlation between the sequence and the signal measured at the lips. The advantages of this method over methods based on sweep-tones or white noise excitation are (1) a much shorter measurement time (about 100 ms) and (2) the possibility of speech sound production during the measurement. This method has been checked against classical methods through systematic comparisons on a small corpus of vowels. Moreover, it has been verified that simultaneous speech sound production does not perturb significantly the measurements. This method should thus be a very helpful tool for the investigation of the acoustic properties of the vocal tract in various cases for vowels.

Prenatal ethanol exposure impairs the conduction delay at the atrioventricular junction in the looping heart

2021 ◽  
Author(s):  
Shan Ling ◽  
Michael W Jenkins ◽  
Michiko Watanabe ◽  
Stephanie M Ford ◽  
Andrew M Rollins
Keyword(s):  
Heart Development ◽  
Molecular Mechanisms ◽  
Early Stage ◽  
Heart Defects ◽  
Ethanol Exposure ◽  
Cardiac Conduction ◽  
Conduction Delay ◽  
Prenatal Ethanol Exposure ◽  
Endocardial Cushions ◽  
Atrioventricular Junction

The etiology of ethanol-related congenital heart defects has been the focus of much study, but most research has concentrated on cellular and molecular mechanisms. We have shown with optical coherence tomography (OCT) that ethanol exposure led to increased retrograde flow and smaller atrioventricular (AV) cushions compared to controls. Since AV cushions play a role in patterning the conduction delay at the atrioventricular junction (AVJ), this study aims to investigate whether ethanol exposure alters the AVJ conduction in early looping hearts and whether this alteration is related to the decreased cushion size. Quail embryos were exposed to a single dose of ethanol at gastrulation, and Hamburger-Hamilton stage 19 - 20 hearts were dissected for imaging. Cardiac conduction was measured using an optical mapping microscope and we imaged the endocardial cushions using OCT. Our results showed that, compared with controls, ethanol-exposed embryos exhibited abnormally fast AVJ conduction and reduced cushion size. However, this increased conduction velocity (CV) did not strictly correlate with decreased cushion volume and thickness. By matching the CV map to the cushion size map, we found that the slowest conduction location was consistently at the atrial side of the AVJ, which had the thinner cushions, not at the thickest cushion location at the ventricular side as expected. Our findings reveal regional differences in the AVJ myocardium even at this early stage in heart development. These findings reveal the early steps leading to the heterogeneity and complexity of conduction at the mature AVJ, a site where arrhythmias can be initiated.

Correlation between vocal tract length, body height, formant frequencies, and pitch frequency for the five Japanese vowels uttered by fifteen male speakers

2012 ◽  
Author(s):  
Hiroaki Hatano ◽  
Tatsuya Kitamura ◽  
Hironori Takemoto ◽  
Parham Mokhtari ◽  
Kiyoshi Honda ◽  
...  
Keyword(s):  
Vocal Tract ◽  
Body Height ◽  
Tract Length ◽  
Formant Frequencies ◽  
Pitch Frequency

i-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques

2018 ◽  
Vol 29 (1) ◽  
pp. 565-582
Author(s):  
T.R. Jayanthi Kumari ◽  
H.S. Jayanna
Keyword(s):  
Linear Prediction ◽  
Vocal Tract ◽  
Speaker Verification ◽  
Excitation Source ◽  
Limited Data ◽  
Score Level Fusion ◽  
Verification System ◽  
Modeling Techniques ◽  
Prediction Residual ◽  
Level Fusion

Abstract In many biometric applications, limited data speaker verification plays a significant role in practical-oriented systems to verify the speaker. The performance of the speaker verification system needs to be improved by applying suitable techniques to limited data condition. The limited data represent both train and test data duration in terms of few seconds. This article shows the importance of the speaker verification system under limited data condition using feature- and score-level fusion techniques. The baseline speaker verification system uses vocal tract features like mel-frequency cepstral coefficients, linear predictive cepstral coefficients and excitation source features like linear prediction residual and linear prediction residual phase as features along with i-vector modeling techniques using the NIST 2003 data set. In feature-level fusion, the vocal tract features are fused with excitation source features. As a result, on average, equal error rate (EER) is approximately equal to 4% compared to individual feature performance. Further in this work, two different types of score-level fusion are demonstrated. In the first case, fusing the scores of vocal tract features and excitation source features at score-level-maintaining modeling technique remains the same, which provides an average reduction approximately equal to 2% EER compared to feature-level fusion performance. In the second case, scores of the different modeling techniques are combined, which has resulted in EER reduction approximately equal to 4.5% compared with score-level fusion of different features.

scholarly journals Analysis of Separability of COVID-19 and Pneumonia in Chest X-ray Images by Means of Convolutional Neural Networks

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Symptoms ◽  
Early Stage ◽  
World Health ◽  
Complete Analysis ◽  
Screening Process ◽  
X Ray ◽  
Chest X Ray ◽  
Health Organization

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.

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