TY  - JOUR
T1  - Cross Validation of Machine Learning Classifiers and
Features for Audio Forensics Verification
AU - Kevin Segura, Jhon AU - Renza, Diego AU - Dora M. Ballesteros, L. 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 12
SP  - 4512
EP  - 4517
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.4512.4517
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.4512.4517
KW  - Speaker recognition
KW  -Mel-Frequencies Cepstral Coefficients (MFCC)
KW  -cochleagram
KW  -feature
KW  -selection
KW  -classiffier evaluation
KW  -assessment
AB  - In literature, there are several manuscripts related to finding the best feature or the best classifier for
audio verification systems. However, cross validation with both criteria has not been widely discussed. In this
research, 15 classifiers and six features have been selected to obtain ninety options for audio forensics
verification. The aim is to provide suggested combinations for forensics researches. The evaluated classifiers
are based on decision trees, discriminant analysis, support vector machines, nearest neighbour and hybrid
classifiers. The feature extraction is based on Mel-Frequency Cepstral Coefficients (MFCC) and cochleagrams,
using principal component analysis optionally. The tests are performed on a database of 50 speakers and 10
utterances per speaker and the assessment of classifiers is made by means of accuracy. According to the
results, the best combination is MFCC with linear discrimination, followed very close by MFCC+PCA with linear
discriminant.
ER  -