Voice Signal Analysis with the Application in Biomedicine

2020 ◽  
Vol 18 (2) ◽  
pp. 122-127
Author(s):  
Vikas Mittal ◽  
R. K. Sharma
Keyword(s):  
Signal Analysis ◽  
Voice Quality ◽  
Support Vector ◽  
Efficiency Improvement ◽  
Average Efficiency ◽  
Voice Signal ◽  
Signal Parameters ◽  
Nearest Neighbours ◽  
Voice Classification ◽  
Pathological Voice

Voice pathology is the result of improper vocal use. Poor vocal exercise and repeated laryngeal infection may lead to worse voice quality and vocal stresses. This work uses glottal signal parameters obtained from speakers of distinct ages to identify voice disorders. The parameters obtained from the glottal signal, Mel Frequency Cepstrum Coefficients (MFCCs) and combination of glottal and MFFCs are used for pathological voice classification. Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) algorithms are used. Results show that best classification results are achieved using combinations of MFFCs and with glottal parameters including MOQ, which is a novel outcome and most important involvement of this study, with an average efficiency improvement of 3%.

scholarly journals Is It Possible to Forecast the Price of Bitcoin?

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 377-420
Author(s):  
Julien Chevallier ◽  
Dominique Guégan ◽  
Stéphane Goutte
Keyword(s):  
Data Analytics ◽  
Daily Variation ◽  
A Priori ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Market Growth ◽  
Network Support ◽  
Market Participants ◽  
Nearest Neighbours ◽  
Stationary Behavior

This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

scholarly journals Modelling of Amplitude Modulated Vocal Fry Glottal Area Waveforms Using an Analysis-by-Synthesis Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 1990
Author(s):  
Vinod Devaraj ◽  
Philipp Aichinger
Keyword(s):  
Voice Quality ◽  
Low Frequency ◽  
Voice Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Power Spectral ◽  
Analysis By Synthesis ◽  
Simple Support ◽  
Frequency Modulations ◽  
Synthesis Approach

The characterization of voice quality is important for the diagnosis of a voice disorder. Vocal fry is a voice quality which is traditionally characterized by a low frequency and a long closed phase of the glottis. However, we also observed amplitude modulated vocal fry glottal area waveforms (GAWs) without long closed phases (positive group) which we modelled using an analysis-by-synthesis approach. Natural and synthetic GAWs are modelled. The negative group consists of euphonic, i.e., normophonic GAWs. The analysis-by-synthesis approach fits two modelled GAWs for each of the input GAW. One modelled GAW is modulated to replicate the amplitude and frequency modulations of the input GAW and the other modelled GAW is unmodulated. The modelling errors of the two modelled GAWs are determined to classify the GAWs into the positive and the negative groups using a simple support vector machine (SVM) classifier with a linear kernel. The modelling errors of all vocal fry GAWs obtained using the modulating model are smaller than the modelling errors obtained using the unmodulated model. Using the two modelling errors as predictors for classification, no false positives or false negatives are obtained. To further distinguish the subtypes of amplitude modulated vocal fry GAWs, the entropy of the modulator’s power spectral density and the modulator-to-carrier frequency ratio are obtained.

A New Model-Based Tool for Fault Detection and Isolation in Machine- and Rotordynamics

1995 ◽  
Author(s):  
Dirk Söffker
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Signal Analysis ◽  
Power Plants ◽  
Crack Detection ◽  
Fault Detection And Isolation ◽  
Vibration Monitoring ◽  
Monitoring Systems ◽  
Implicit Assumption ◽  
Signal Parameters

Abstract Reliability and safety aspects are becoming much more important due to higher quality requirements, complicated and/or connected processes. The fault monitoring systems to be commonly used in machine- and rotordynamics are based on signal analysis methods. Furthermore, various kinds of fault detection and isolation (FDI)-schemes are already applied to a lot of technical applications of detecting and isolating sensor and actuator failures (Isermann, 1994; van Schrick, 1994) and also to fault detection in power plants (in general) or in manufacturing machines. An implicit assumption is that process or machine changes due to faults lead to changes in calculated parameters, which are unique and unambiguous. In the case of applying methods of signal analysis this means spectrums etc. the vibration behaviour will be monitored very well but have to be interpreted. On the other hand signal parameters usually only describe the system by analyzing output signals without use of known and unknown inner parameters and/or inputs. These parameters are available, and normally this knowledge is used by the operating staff interpreting the resulting signal parameters. In this way a decision-making problem appears so that questions about the physical character of faults, about the existence of special faults and also about the location of failures/faults has to be answered. In this way the experience and knowledge of the interpreting persons are very important. In this contribution the problems of the decision-making process are tried to defuse: • The available knowledge about the unfaulty system parameters is used to built up beside a nominal system model an unambiguous fault-specific ratio. Inner states of the structure are estimated by an PI-observer. • The developed robust PI-observer (Söffker et al., 1993a; Söffker et al., 1995a) estimates inner states and unknown inputs. In (Söffker et al., 1993b) this new method is applied to the crack detection of a rotor, but not proved. In this paper the proof is given and a generalization is described. The advantages in contrast to usual signal based vibration monitoring systems and also modern FDI-schemes are shown.

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 Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

2019 ◽  
Vol 11 (11) ◽  
pp. 3222 ◽  
Author(s):  
Pascal Schirmer ◽  
Iosif Mporas
Keyword(s):  
Random Forest ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Random Forest Regression ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Vector Machines ◽  
Non Linear ◽  
Load Monitoring ◽  
Sinusoidal Current

In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

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 Comparative Analysis for Topic Classification in Juz Al-Baqarah

2018 ◽  
Vol 12 (1) ◽  
pp. 406
Author(s):  
Mohamad Izzuddin Rahman ◽  
Noor Azah Samsudin ◽  
Aida Mustapha ◽  
Adeleke Abdullahi
Keyword(s):  
Text Mining ◽  
Text Categorization ◽  
Arabic Language ◽  
Support Vector ◽  
Original Text ◽  
Research Project ◽  
Computational Environment ◽  
Nearest Neighbours ◽  
Association Discovery ◽  
Relationship Of

<p>In Islam, Quran is the holy book that was revealed to the Prophet Muhammad. It functions as complete code of life for the Muslims. Remarks from Allah which contains more than 77,000 words that was passed down through Prophet Muhammad to the mankind for 23 years started in 610 ce. The Quran was divided into 114 chapters.  Arabic language is the original text. The need for the Muslims across the world to find the meaning to understand the content in the Quran is necessary. Nevertheless, understanding the Quran is an interest for the Muslims as well as the attention of millions of people from the faiths.  Following the generation, lots of content that related to the Quran has been broadcast by Muslims scholars in the way of the tafsirs, translation and the book of hadiths. Problem has happened at current is most Muslim in Malaysia do not understand sentences in the Quran due to language barrier. The purpose of this research is classified topic in each verses of the Quran sentence based on its specific theme. It involves the objective of text mining which are based on linguistic information and domain. The usage of corpus helps to perform various data mining tasks including information extraction, text categorization, the relationship of concepts, association discovery, the evaluation of pattern and assessed. This research project is aiming to create computing environment that enable us use to text mining the Quran. The classification experiment is using the Support Vector Machine to find themes in Juz’ Baqarah. The SVM performance is then compared against other classification algorithms such as Naive Bayes, J48 Decision Tree and K-Nearest Neighbours. This research project aims at creating an enabling computational environment for text mining the Qur’an and to facilitate users to understand every verse in Juz’ Baqarah.</p>

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