scholarly journals Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine

2018 ◽  
Author(s):  
Maryam Pishgar ◽  
Fazle Karim ◽  
Somshubra Majumdar ◽  
Houshang Darabi
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Voice Classification ◽  
Pathological Voice ◽  
Mel Cepstrum

The Poultry Voice Classification Model Based on EMD and Support Vector Machine

2014 ◽  
Vol 722 ◽  
pp. 217-221
Author(s):  
Dong Kang He ◽  
Shou Ming Zhang ◽  
Gui Hong Bi ◽  
Rui Yu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
High Accuracy ◽  
Classification Model ◽  
Support Vector ◽  
Energy Transformation ◽  
Mode Function ◽  
Mode Decomposition ◽  
Voice Classification ◽  
Teager Energy

According to the non-stationary and non-linear characteristics of poultry voice and the situation that it`s hard to obtain enough sound samples, a poultry voice classification method based on Empirical Mode Decomposition (EMD), Teager energy transformation, and Support Vector Machine (SVM) is proposed. Firstly, the poultry voice signals are decomposed into a finite number of intrinsic mode function (IMF).Then, the Teager energy of five IMFs filtered are used to form characteristic vectors. Finally, the eigenvectors are put into a support vector machine classifier . The results of animal voice signals experimental recognition showed that this method had high accuracy and good generalization abilities even in the case of small number of samples. The approach proposed could identify the poultry voice effectively.

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%.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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