TY - JOUR
T1 - Voice pathology detection using interlaced derivative pattern on glottal source excitation
AU - Muhammad, Ghulam
AU - Alsulaiman, Mansour
AU - Ali, Zulfiqar
AU - Mesallam, Tamer A.
AU - Farahat, Mohamed
AU - Malki, Khalid H.
AU - Al-nasheri, Ahmed
AU - Bencherif, Mohamed A.
PY - 2017/1/31
Y1 - 2017/1/31
N2 - In this paper, we propose a voice pathology detection and classification method using an interlaced derivative pattern (IDP), which involves an n-th order directional derivative, on a spectro-temporal description of a glottal source excitation signal. It is shown previously that directional information is useful to detect pathologies due to its encoding ability along time, frequency, and time-frequency axes. The IDP, being an n-th order derivative, is capable of describing more information than a first order derivative pattern by combining all the directional information into one. In the IDP, first-order derivatives are calculated in four directions, and these derivatives are thresholded with the center value of each directional channel to produce the final IDP. A support vector machine is used as a classification technique. Experiments are conducted using three different databases, which are the Massachusetts Eye and Ear Infirmary database, Saarbrucken Voice Database, and Arabic Voice Pathology Database. Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases. The accuracies using cross-databases are also high using the IDP features.
AB - In this paper, we propose a voice pathology detection and classification method using an interlaced derivative pattern (IDP), which involves an n-th order directional derivative, on a spectro-temporal description of a glottal source excitation signal. It is shown previously that directional information is useful to detect pathologies due to its encoding ability along time, frequency, and time-frequency axes. The IDP, being an n-th order derivative, is capable of describing more information than a first order derivative pattern by combining all the directional information into one. In the IDP, first-order derivatives are calculated in four directions, and these derivatives are thresholded with the center value of each directional channel to produce the final IDP. A support vector machine is used as a classification technique. Experiments are conducted using three different databases, which are the Massachusetts Eye and Ear Infirmary database, Saarbrucken Voice Database, and Arabic Voice Pathology Database. Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases. The accuracies using cross-databases are also high using the IDP features.
KW - AVPD
KW - Glottal source excitation
KW - Interlaced derivative pattern (IDP)
KW - MEEI
KW - SVD
KW - Voice pathology detection
UR - http://www.scopus.com/inward/record.url?scp=84990178804&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2016.08.002
DO - 10.1016/j.bspc.2016.08.002
M3 - Article
AN - SCOPUS:84990178804
SN - 1746-8094
VL - 31
SP - 156
EP - 164
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
ER -