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

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...

Full description

Bibliographic Details
Main Authors:	Aibinu, Abiodun Musa, Salami, Momoh Jimoh Eyiomika, Shafie, Amir Akramin
Format:	Article
Language:	English
Published:	Elsevier Science Ltd. 2012
Subjects:	T Technology (General) TJ Mechanical engineering and machinery
Online Access:	http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/2/VAD-New_offprint-New.pdf

id	iium-25570
recordtype	eprints
spelling	iium-255702012-08-28T00:30:43Z http://irep.iium.edu.my/25570/ Artificial neural network based autoregressive modeling technique with application in voice activity detection Aibinu, Abiodun Musa Salami, Momoh Jimoh Eyiomika Shafie, Amir Akramin T Technology (General) TJ Mechanical engineering and machinery 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. Elsevier Science Ltd. 2012-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/25570/2/VAD-New_offprint-New.pdf 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 http://dl.acm.org/citation.cfm?id=2345031 10.1016/j.engappai.2012.05.012
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General) TJ Mechanical engineering and machinery
spellingShingle	T Technology (General) TJ Mechanical engineering and machinery Aibinu, Abiodun Musa Salami, Momoh Jimoh Eyiomika Shafie, Amir Akramin Artificial neural network based autoregressive modeling technique with application in voice activity detection
description	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.
format	Article
author	Aibinu, Abiodun Musa Salami, Momoh Jimoh Eyiomika Shafie, Amir Akramin
author_facet	Aibinu, Abiodun Musa Salami, Momoh Jimoh Eyiomika Shafie, Amir Akramin
author_sort	Aibinu, Abiodun Musa
title	Artificial neural network based autoregressive modeling technique with application in voice activity detection
title_short	Artificial neural network based autoregressive modeling technique with application in voice activity detection
title_full	Artificial neural network based autoregressive modeling technique with application in voice activity detection
title_fullStr	Artificial neural network based autoregressive modeling technique with application in voice activity detection
title_full_unstemmed	Artificial neural network based autoregressive modeling technique with application in voice activity detection
title_sort	artificial neural network based autoregressive modeling technique with application in voice activity detection
publisher	Elsevier Science Ltd.
publishDate	2012
url	http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/ http://irep.iium.edu.my/25570/2/VAD-New_offprint-New.pdf
first_indexed	2023-09-18T20:38:06Z
last_indexed	2023-09-18T20:38:06Z
_version_	1777409181915021312

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

Similar Items