IEICE TRANSACTIONS on Fundamentals
A Nonlinear Spectrum Estimation System Using RBF Network Modified for Signal Processing
Summary :
This paper proposes a nonlinear signal processing by using a three layered network which is trained with self-organized clustering and supervised learning. The network consists of three layers, i.e., self-organized layer, an evaluation layer and an output layer. Since the evaluation layer is designed as a simple perceptron network and the output layer is designed as a fixed weight linear node, the training complexity is the same as a conventional one consisting of self-organized clustering and a simple perceptron network. In other words, quite high speed training can be realized. Generally speaking, since the data range is arbitrary large in signal procession, the network shoulk cover this range and output a value as accurately as possible. However, it may be hard for only a node in the network to output these data. Instead of this mechanism, if this dynamic range is covered by using several nodes, the complexity of each node is reduced and the associated range is also limited. This results on the higher performance of the network than conventional RBFs. This paper introduces a new non-linear spectrum estimation which consists of LPC analysis and RBF network. It is shown that accuracy spectrum envelopes can be obtained since a new RBF network can estimate some nonlinearities in a speech production.
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- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.8 pp.1460-1466
- Publication Date
- 1997/08/25
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- Type of Manuscript
- Special Section PAPER (Special Section on Digital Signal Processing)
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Hideaki IMAI, Yoshikazu MIYANAGA, Koji TOCHINAI, "A Nonlinear Spectrum Estimation System Using RBF Network Modified for Signal Processing" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 8, pp. 1460-1466, August 1997, doi: .
Abstract: This paper proposes a nonlinear signal processing by using a three layered network which is trained with self-organized clustering and supervised learning. The network consists of three layers, i.e., self-organized layer, an evaluation layer and an output layer. Since the evaluation layer is designed as a simple perceptron network and the output layer is designed as a fixed weight linear node, the training complexity is the same as a conventional one consisting of self-organized clustering and a simple perceptron network. In other words, quite high speed training can be realized. Generally speaking, since the data range is arbitrary large in signal procession, the network shoulk cover this range and output a value as accurately as possible. However, it may be hard for only a node in the network to output these data. Instead of this mechanism, if this dynamic range is covered by using several nodes, the complexity of each node is reduced and the associated range is also limited. This results on the higher performance of the network than conventional RBFs. This paper introduces a new non-linear spectrum estimation which consists of LPC analysis and RBF network. It is shown that accuracy spectrum envelopes can be obtained since a new RBF network can estimate some nonlinearities in a speech production.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_8_1460/_p
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@ARTICLE{e80-a_8_1460,
author={Hideaki IMAI, Yoshikazu MIYANAGA, Koji TOCHINAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Nonlinear Spectrum Estimation System Using RBF Network Modified for Signal Processing},
year={1997},
volume={E80-A},
number={8},
pages={1460-1466},
abstract={This paper proposes a nonlinear signal processing by using a three layered network which is trained with self-organized clustering and supervised learning. The network consists of three layers, i.e., self-organized layer, an evaluation layer and an output layer. Since the evaluation layer is designed as a simple perceptron network and the output layer is designed as a fixed weight linear node, the training complexity is the same as a conventional one consisting of self-organized clustering and a simple perceptron network. In other words, quite high speed training can be realized. Generally speaking, since the data range is arbitrary large in signal procession, the network shoulk cover this range and output a value as accurately as possible. However, it may be hard for only a node in the network to output these data. Instead of this mechanism, if this dynamic range is covered by using several nodes, the complexity of each node is reduced and the associated range is also limited. This results on the higher performance of the network than conventional RBFs. This paper introduces a new non-linear spectrum estimation which consists of LPC analysis and RBF network. It is shown that accuracy spectrum envelopes can be obtained since a new RBF network can estimate some nonlinearities in a speech production.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - A Nonlinear Spectrum Estimation System Using RBF Network Modified for Signal Processing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1460
EP - 1466
AU - Hideaki IMAI
AU - Yoshikazu MIYANAGA
AU - Koji TOCHINAI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1997
AB - This paper proposes a nonlinear signal processing by using a three layered network which is trained with self-organized clustering and supervised learning. The network consists of three layers, i.e., self-organized layer, an evaluation layer and an output layer. Since the evaluation layer is designed as a simple perceptron network and the output layer is designed as a fixed weight linear node, the training complexity is the same as a conventional one consisting of self-organized clustering and a simple perceptron network. In other words, quite high speed training can be realized. Generally speaking, since the data range is arbitrary large in signal procession, the network shoulk cover this range and output a value as accurately as possible. However, it may be hard for only a node in the network to output these data. Instead of this mechanism, if this dynamic range is covered by using several nodes, the complexity of each node is reduced and the associated range is also limited. This results on the higher performance of the network than conventional RBFs. This paper introduces a new non-linear spectrum estimation which consists of LPC analysis and RBF network. It is shown that accuracy spectrum envelopes can be obtained since a new RBF network can estimate some nonlinearities in a speech production.
ER -
English
