Particle Swarm Optimisation of Mel-frequency Cepstral Coefficients computation for the classification of asphyxiated infant cry

2010 ◽  
Author(s):  
A. Zabidi ◽  
W. Mansor ◽  
Y. K. Lee ◽  
A. I. Mohd Yassin ◽  
R. Sahak
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Mel Frequency Cepstral Coefficients ◽  
Infant Cry ◽  
Cepstral Coefficients

scholarly journals Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ömer Eskidere ◽  
Ahmet Gürhanlı
Keyword(s):  
Voice Disorder ◽  
Mel Frequency Cepstral Coefficients ◽  
Essential Information ◽  
Audio Processing ◽  
Voice Signal ◽  
Window Technique ◽  
Normal Voice ◽  
Cepstral Coefficients ◽  
Better Than

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.

STATISTICAL VECTORS OF ACOUSTIC FEATURES FOR THE AUTOMATIC CLASSIFICATION OF INFANT CRY

2007 ◽  
Vol 04 (04) ◽  
pp. 347-355 ◽  
Author(s):  
ERIKA AMARO-CAMARGO ◽  
CARLOS A. REYES-GARCÍA ◽  
EMILIO ARCH-TIRADO ◽  
MARIO MANDUJANO-VALDÉS
Keyword(s):  
Majority Vote ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Acoustic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Average Standard Deviation ◽  
Infant Cry ◽  
Vector Machines ◽  
Data Reduction Technique

With the objective of helping diagnose some pathologies in recently born babies, we present the experiments and results obtained in the classification of infant cry using a variety of single classifiers, and ensembles from the combination of them. Three kinds of cry were classified: normal, hypoacoustic (deaf), and asphyxia. The feature vectors were formed by the extraction of Mel Frequency Cepstral Coefficients (MFCC). The vectors were then processed and reduced through the application of five statistics operations, namely: minimum, maximum, average, standard deviation and variance. LDA, a data reduction technique is implemented with the purpose of comparing the results of our proposed method. Four supervised machine learning methods including Support Vector Machines, Neural Networks, J48, Random Forest and Naive Bayes are used. The ensembles tested were combinations of these under different approaches like Majority Vote, Staking, Bagging and Boosting.

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