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

Authors:
A. M. Aibinu;M. J. E. Salami;A. A. Shafie
Affiliations:
International Islamic University Malaysia (IIUM), Department of Mechatronics, Gombak, P.O. BOX 53100 Gombak, Malaysia;International Islamic University Malaysia (IIUM), Department of Mechatronics, Gombak, P.O. BOX 53100 Gombak, Malaysia;International Islamic University Malaysia (IIUM), Department of Mechatronics, Gombak, P.O. BOX 53100 Gombak, Malaysia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2012

Citing 11
Cited 0

Adaptive filter theory

Adaptive filter theory
Digital spectral analysis: with applications

Digital spectral analysis: with applications
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Learning internal representations by error propagation

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Statistical Digital Signal Processing and Modeling

Statistical Digital Signal Processing and Modeling
Applied Neural Networks for Signal Processing

Applied Neural Networks for Signal Processing
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)

Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks (Studies in Computational Intelligence)

Complex-Valued Neural Networks (Studies in Computational Intelligence)
Complex-valued neural networks: the merits and their origins

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ARMA model order estimation based on the eigenvalues of thecovariance matrix

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

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.