Pathological Voice Classification Based on Wavelet Packet Multiscale Analysis

2018 ◽  
Author(s):  
Xuehui Zhang ◽  
Weiping Hu
Keyword(s):  
Multiscale Analysis ◽  
Wavelet Packet ◽  
Voice Classification ◽  
Pathological Voice

Ensemble and Multimodal Learning for Pathological Voice Classification

2021 ◽  
pp. 1-1
Author(s):  
Whenty Ariyanti ◽  
Tassadaq Hussain ◽  
Jia-Ching Wang ◽  
Chi-Tei Wang ◽  
Shih-Hau Fang ◽  
...  
Keyword(s):  
Multimodal Learning ◽  
Voice Classification ◽  
Pathological Voice

Pathological Voice Classification Based on Recurrence Quantification Measures

2012 ◽  
Author(s):  
Washington Costa ◽  
F. Assis ◽  
B. Neto ◽  
Silvana Costa ◽  
Vinı́cius Vieira
Keyword(s):  
Recurrence Quantification ◽  
Voice Classification ◽  
Pathological Voice

A GENERALIZED MULTISCALE ANALYSIS OF THE PREDICTIVE CONTENT OF EURODOLLAR IMPLIED VOLATILITIES

2009 ◽  
Vol 12 (01) ◽  
pp. 1-18 ◽  
Author(s):  
ALESSANDRO CARDINALI
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Wavelet Packet ◽  
Discrete Wavelet ◽  
Predictive Information ◽  
Wide Range ◽  
Predictive Content ◽  
Periodic Components

It is widely believed that implied volatilities contains information that would enable prediction of spot volatility for a wide range of financial assets. Lead-lag analysis based on the Discrete Wavelet Transform has been proposed as one method for identifying and extracting that predictive information. Unfortunately this approach can fail to identify periodic components that are not proportional to an increasing dyadic scale. We propose a multiscale analysis of the Eurodollar realized volatility and at-the-money (ATM) implied volatilities. After filtering the long memory components we produce a decomposition of cross-correlation by using wavelet packet methods. A threshold cost functional based on asymptotic confidence intervals was used along with the best basis algorithm in order to select an adaptive frequency partition of the sample cross-correlation. We found substantial evidence that Eurodollar implied volatilities contain predictive information about realized volatilities. Moreover, in our analysis the new technique outperforms the lead-lag analysis based on the nondecimated Discrete Wavelet Transform. Therefore we contend that the proposed technique will improve detection of predictive information and recommend further testing in a range of applied contexts.

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%.

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