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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Science Ltd.
2012
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/25570/2/VAD-New_offprint-New.pdf http://irep.iium.edu.my/25570/ http://dl.acm.org/citation.cfm?id=2345031 |
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| Summary: | 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. |
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