Nonlinear dynamics of the voice: Signal analysis and biomechanical modeling

1995 ◽  
Vol 5 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Hanspeter Herzel ◽  
David Berry ◽  
Ingo Titze ◽  
Ina Steinecke
Keyword(s):  
Nonlinear Dynamics ◽  
Signal Analysis ◽  
Biomechanical Modeling ◽  
Voice Signal ◽  
The Voice

scholarly journals Speech Signal Analysis and Classification of Dominant Parameter for Pathological Voices

2020 ◽  
Vol 9 (8) ◽  
pp. 225-231
Keyword(s):  
Signal Analysis ◽  
Healthy Person ◽  
Primary Objective ◽  
Age And Gender ◽  
Voice Signal ◽  
Secondary Objective ◽  
And Gender ◽  
Learning Software ◽  
The Voice

The primary objective of the project is to analyze speech signals by determining the important parameters that affect the voice of an individual which leads to various voice disorders. The analysis is carried out based on the individual’s age and gender with the help of the pattern recognized from each sample and the value of each parameter is compared with the nominal values of the healthy person with respect to their age and gender using the Praat software. The secondary objective is the classification of the voice signal into normal and abnormal voice samples using the machine learning software Konstanz Information Miner (KNIME).

scholarly journals Analysis of Voice Signals Change by Voice Modulation Program

2018 ◽  
Vol 7 (3.34) ◽  
pp. 506
Author(s):  
Bong Hyun Kim ◽  
. .
Keyword(s):  
Communication Technology ◽  
Signal Analysis ◽  
Voice Analysis ◽  
Voice Signal ◽  
Significant Difference ◽  
Normal Voice ◽  
Information And Communication ◽  
The Difference ◽  
The Voice ◽  
Voice Modulation

Background/Objectives: Voice modulation is used in various fields. Especially, it is widely used in entertainment program for voice tampering to give viewers fun, and voice tampering to guarantee the victim 's identity in news. However, in recent years, voice tampering has been exploited for crime. As the information and communication technology in the information society has developed rapidly, the crime using voice modulation is increasing.Methods/Statistical analysis: Therefore, in this paper, the change of voice signal is analyzed by analyzing both normal voice and modulated voice. For this purpose, general voice was collected using the same place, time, microphone, etc., and a modulated voice was collected by applying a voice modulation program. In addition, various voice analysis parameters such as spectrum, formant, intensity, pitch, pulse, jitter, shimmer, DoVB, and NHR were applied to the study.Findings: Experimental results show that the difference between the normal voice and the modulated voice is caused by various voice signal analysis parameters due to voice modulation. Especially, in the modulated voice, the spectrum, pitch, and DoVB values were decreased as compared with the general voice. In addition, jitter, shimmer, and NHR values resulted in a result that the modulated voice was higher than the normal voice. There was no significant difference in strength, formant and pulse measurements. Based on the results of this study, it is possible to reflect the changed voice analysis parameters by the voice modulation program.Improvements/Applications: Voice modulation is useful in various aspects such as fun and identification. However, it has been recently exploited in the same way as voice phishing. Therefore, in this paper, we measured the voice analysis parameters that are changed by the voice modulation program. Based on this, we compared and analyzed the general voice and the modulated voice and extracted the pattern of the voice signal changed by the voice modulation. 

Voice Signal Noise Reduction Based on Hilbert-Huang Transform

2013 ◽  
Vol 303-306 ◽  
pp. 1039-1042
Author(s):  
Feng Lei Ma ◽  
Xiao Long Zhou ◽  
Yong Tao Zheng
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Gaussian Noise ◽  
Signal Analysis ◽  
Noise Signal ◽  
Analytic Method ◽  
Signal Noise ◽  
Hilbert Huang Transform ◽  
Voice Signal ◽  
The Voice

In this paper, we studied voice signal with Gaussian noise reduction. Based on signal analysis and reconstruction principle. Application of Hilbert-Huang transform(HHT), analyzed voice signal with Gaussian noise. And comprised with wavelet transform(WT), obtained the correlation coefficient between HHT noise reduction signal and excluding the noise signal was 0.8986, WT was 0.7889. The results showed that in the voice signal with Gaussian noise, compared HHT and wavelet analysis correlation analysis values, HHT noise reduction ability was 10% higher than WT. This paper provided a new analytic method to the voice signal noise reduction and enhanced the accuracy of it.

scholarly journals Non-invasive vocal biomarker is associated with pulmonary hypertension

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Sara ◽  
E Maor ◽  
B Borlaug ◽  
D Orbelo ◽  
L Lerman ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Signal Analysis ◽  
Health Care Systems ◽  
A Priori ◽  
Intraclass Correlation ◽  
Funding Source ◽  
Non Invasive ◽  
Voice Signal ◽  
The Impact ◽  
The Voice

Abstract Background Heart failure (HF) is a debilitating disease and is associated with significant morbidity and mortality, and costs to health care systems. Monitoring the impact of therapy remotely holds the potential to reduce HF-related hospitalizations, improve quality of life, and optimize the use of the limited resources. Voice signal is an emerging non-invasive biomarker that has been associated with a number of disease states. We previously identified a significant relationship between specific vocal biomarkers and coronary artery disease, and recently extended these observations by showing that a pre-specified voice biomarker was associated with increased mortality and re-hospitalization in patients with HF. Purpose In the current study, we evaluated the association between a vocal biomarker derived from voice signal analysis and invasively measured indices of pulmonary hypertension (PH). We hypothesized that the pre-specified voice biomarker might be associated with hemodynamic indices reflective of PH that are known to be linked to HF-severity, and that predict outcomes such as HF-related hospitalization and death. Methods The study population included patients referred for an invasive cardiac hemodynamic study between January 2017 and December 2018. Subjects had their voice signal recorded to their smartphone on three separate occasions prior to the cardiac study. A pre-established numeric vocal biomarker was derived from each recording, and the mean vocal biomarker calculated for each patient. Patients were a priori divided into two groups: those with high pulmonary arterial pressure (PAP) defined as ≥35 mmHg consistent with moderate or greater PH, versus those with a lower PAP. Results Eighty three patients, mean age 61.6±15.1 years, 37 (44.6%) male, were included in the study. The intraclass correlation coefficient for the vocal biomarker in all patients was 0.83 implying very good agreement between values. Patients with a high mean PAP (≥35 mmHg) had significantly higher values of the voice biomarker compared to those with a lower mean PAP (0.74±0.85 vs. 0.43±0.86, p=0.008). Patients with a high pulmonary vascular resistance (PVR) defined as a PVR ≥1.7 Wood Units had significantly higher values of the voice biomarker compared to those with a lower PVR (0.62±0.83 vs. 0.33±0.90, p=0.026). Multivariable logistic regression showed that an increase in the voice biomarker by 1 unit was significantly associated with a high PAP, odds ratio (OR) 2.31, 95% CI 1.05–5.07, p=0.038, and with borderline significance with a high PVR, OR 2.14, 95% CI 0.94–4.87, p=0.07. Conclusion The current study shows a relationship between a noninvasive vocal biomarker derived from voice signal analysis and invasively derived hemodynamic indices related to PH obtained during cardiac catheterization. These results may have important and practical clinical implications for telemedicine and remote monitoring of patients with HF and PH. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Mayo Foundation; Beyond Verbal Communications

scholarly journals Pathological Voice Signal Analysis Using Machine Learning Based Approaches

2017 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Yahia Alemami ◽  
Laiali Almazaydeh
Keyword(s):  
Signal Analysis ◽  
Naive Bayes ◽  
Neurodegenerative Disorder ◽  
Speech Disorders ◽  
Naïve Bayes ◽  
Human Observer ◽  
Automated Classification ◽  
Predictive Tool ◽  
K Nearest Neighbors ◽  
Voice Signal

Voice signal analysis is becoming one of the most significant examination in clinical practice due to the importance of extracting related parameters to reflect the patient's health. In this regard, various acoustic studies have been revealed that the analysis of laryngeal, respiratory and articulatory function may be efficient as an early indicator in the diagnosis of Parkinson disease (PD). PD is a common chronic neurodegenerative disorder, which affects a central nervous system and it is characterized by progressive loss of muscle control. Tremor, movement and speech disorders are the main symptoms of PD. The diagnosis decision of PD is obtained by continued clinical observation which relies on expert human observer. Therefore, an additional diagnosis method is desirable for most comfortable and timely detection of PD as well as faster treatment is needed. In this study, we develop and validate automated classification algorithms, which are based on Naïve Bayes and K- Nearest Neighbors (KNN) using voice signal measurements to predict PD. According to the results, the diagnostic performance provided by the automated classification algorithm using Naïve Bayes was superior to that of the KNN and it is useful as a predictive tool for PD screening with a high degree of accuracy, approximately 93.3%.

scholarly journals Formant structure of the voice during the intensive acute hypoxia

2003 ◽  
Vol 60 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Jovisa Obrenovic ◽  
Milkica Nesic ◽  
Vladimir Nesic ◽  
Snezana Cekic
Keyword(s):  
Speech Signal ◽  
Signal Analysis ◽  
Acute Hypoxia ◽  
Initial Period ◽  
Formant Frequencies ◽  
Hypoxia Exposure ◽  
Structure Changes ◽  
The Voice ◽  
Reversed Order ◽  
Different Altitudes

The influence of intensive acute hypoxia on the frequency-amplitude formant vocal O characteristics was investigated in this study. Examinees were exposed to the simulated altitudes of 5 500 m and 6 700 m in climabaro chamber and resolved Lotig?s test in the conditions of normoxia, i.e. pronounced the three-digit numbers beginning from 900, but in reversed order. Frequency and intensity values of vocal O (F1, F2, F3 and F4) extracted from the context of the pronunciation of the word eight (osam in Serbian), were measured by spectral speech signal analysis. Changes in frequency values and the intensity of the formants were examined. The obtained results showed that there were no significant changes of the formant frequencies in hypoxia condition compared to normoxia. Though significant changes of formant?s intensities were found compared to normoxia on the cited altitudes. The rise of formants intensities was found at the altitude of 5 500 m. Hypoxia at the altitude of 6 700 m caused the significant fall of the intensities in the initial period, compared to normoxia. The prolonged hypoxia exposure caused the rise of the formant intensities compared to the altitude of 5 500 m. In may be concluded that due to different altitudes, hypoxia causes different effects on the formants structure changes, compared to normoxia.

scholarly journals A Machine Learning Approach for Speech Detection in Modern Wireless Communication Environment

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Shibanee Dash . ◽  
Mihir Narayan Mohanty .
Keyword(s):  
Wireless Communication ◽  
Speech Signal ◽  
Symbol Error Rate ◽  
Speech Communication ◽  
Noise Levels ◽  
Speech Detection ◽  
Voice Signal ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
The Voice

Modern wireless communication has gained a improved position as compared to previous time. Similarly, speech communication is the major focus area of research in respective applications. Many developments are done in this field. In this work, we have chosen the OFDM modulation based communication system, as it has importance in both licensed and unlicensed wireless communication platform. The voice signal is passed though the proposed model to obtain at the receiver end. Due to different circumstances, the signal may be corrupted partially at the user end. Authors try to achieve a better signal for reception using a neural network model of RBFN. The parameters are chosen for the RBFN model, as energy, ZCR, ACF, and fundamental frequency of the speech signal. In one part these parameters have eligibility to eliminate noise partially, where as in other part the RBFN model with these parameters proves its efficacy for both noisy speech signals with noisy channel as Gaussian channel. The efficiency of OFDM model is verified in terms of symbol error rate and the transmitted speech signal is evaluated in term of SNR that shows the reduction of noise. For visual inspection, a sample of signal, noisy signal and received signal is also shown. The experiment is performed with 5dB, 10dB, 15dB noise levels. The result proves the performance of RBFN model as the filter.The performance is measured as the listener’s voice in each condition. The results show that, at the time of the voice in noise environment, proposed technique improves the intelligibility on speech quality.

scholarly journals Voice re-education and its evaluation through digital signal processing: A case study

Loquens ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 040
Author(s):  
Zulema Santana-López ◽  
Óscar Domínguez-Jaén ◽  
Jesús B. Alonso ◽  
María Del Carmen Mato-Carrodeguas
Keyword(s):  
Voice Quality ◽  
Quality Measurement ◽  
Digital Signal ◽  
Communication Process ◽  
Time Frequency ◽  
Voice Signal ◽  
Functional Dysphonia ◽  
Before And After ◽  
Voice Pathologies ◽  
The Voice

Voice pathologies, caused either by functional dysphonia or organic lesions, or even by just an inappropriate emission of the voice, may lead to vocal abuse, affecting significantly the communication process. The present study is based on the case of a single patient diagnosed with myasthenia gravis (Erb-Goldflam syndrome). In this case, this affection has caused, among other disruptions, a dysarthria. For its treatment, a technique for the education and re-education of the voice has been used, based on a resonator element: the cellophane screen. This article shows the results obtained in the patient after applying a vocal re-education technique called the Cimardi Method: the Cellophane Screen, which is a pioneering technique in this field. Changes in the patient’s voice signal have been studied before and after the application of the Cimardi Method in different domains of study: time-frequency, spectrum, and cepstrum. Moreover, parameters for voice quality measurement, such as shimmer, jitter and harmonic-to-noise ratio (HNR), have been used to quantify the results obtained with the Cimardi Method. Once the results were analyzed, it has been observed that the Cimardi Method helps to produce a more natural and free vocal emission, which is very useful as a rehabilitation therapy for those people presenting certain vocal disorders.

scholarly journals A Hands-Free Obstacle Detection and Avoidance System

2015 ◽  
Vol 5 (3) ◽  
pp. 801-804
Author(s):  
M. Abdul-Niby ◽  
M. Alameen ◽  
O. Irscheid ◽  
M. Baidoun ◽  
H. Mourtada
Keyword(s):  
Obstacle Avoidance ◽  
Visually Impaired ◽  
Low Cost ◽  
Cost Effective ◽  
Obstacle Detection ◽  
Text To Speech ◽  
Voice Signal ◽  
Visually Impaired People ◽  
Impaired People ◽  
The Voice

In this paper, we present a low cost hands-free detection and avoidance system designed to provide mobility assistance for visually impaired people. An ultrasonic sensor is attached to the jacket of the user and detects the obstacles in front. The information obtained is transferred to the user through audio messages and also by a vibration. The range of the detection is user-defined. A text-to-speech module is employed for the voice signal. The proposed obstacle avoidance device is cost effective, easy to use and easily upgraded.

Application of Local Intrinsic Dimension for Acoustical Analysis of Voice Signal Components

2018 ◽  
Vol 127 (9) ◽  
pp. 588-597 ◽  
Author(s):  
Boquan Liu ◽  
Evan Polce ◽  
Jack Jiang
Keyword(s):  
High Dimensional ◽  
Comprehensive Description ◽  
Intrinsic Dimension ◽  
High Data ◽  
Voice Signal ◽  
Enhanced Resolution ◽  
Limited Effectiveness ◽  
Low Dimensional ◽  
Type 3 ◽  
The Voice

Purpose: The overall aim of this study was to apply local intrinsic dimension ( Di) estimation to quantify high-dimensional, disordered voice and discriminate between the 4 types of voice signals. It was predicted that continuous Di analysis throughout the entire time-series would generate comprehensive descriptions of voice signal components, called voice type component profiles (VTCP), that effectively distinguish between the 4 voice types. Method: One hundred thirty-five voice recording samples of the sustained vowel /a/ were obtained from the Disordered Voice Database Model 4337 and spectrographically classified into the voice type paradigm. The Di and correlation dimension ( D2) were then used to objectively analyze the voice samples and compared based on voice type differentiation efficacy. Results: The D2 exhibited limited effectiveness in distinguishing between the 4 voice type signals. For Di analysis, significant differences were primarily observed when comparing voice type component 1 (VTC1) and 4 (VTC4) across the 4 voice type signals ( P < .001). The 4 voice type components (VTCs) significantly differentiated between low-dimensional, type 3 and high-dimensional, type 4 signals ( P < .001). Conclusions: The Di demonstrated improvements over D2 in 2 distinct manners: enhanced resolution at high data dimensions and comprehensive description of voice signal elements.

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