Artificial neural network based autoregressive modeling technique with application in voice activity detection

Aibinu, Abiodun Musa and Salami, Momoh Jimoh Eyiomika and Shafie, Amir Akramin (2012) Artificial neural network based autoregressive modeling technique with application in voice activity detection. Engineering Applications of Artificial Intelligence, 25 (6). pp. 1265-1276. ISSN 0952-1976

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Abstract

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech.

Item Type:	Article (Journal)
Additional Information:	6472/25570
Uncontrolled Keywords:	autoregressive model, real-valued neural network (RVNN), unvoiced signals, voiced activity detection, voiced signals
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Dr Abiodun Musa Aibinu
Date Deposited:	28 Aug 2012 08:30
Last Modified:	28 Aug 2012 08:30
URI:	http://irep.iium.edu.my/id/eprint/25570

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