H. Imai et al., A NONLINEAR SPECTRUM ESTIMATION SYSTEM USING RBF NETWORK MODIFIED FORSIGNAL-PROCESSING, IEICE transactions on fundamentals of electronics, communications and computer science, E80A(8), 1997, pp. 1460-1466
Citations number
15
Categorie Soggetti
Engineering, Eletrical & Electronic","Computer Science Hardware & Architecture","Computer Science Information Systems
This paper proposes a nonlinear signal processing by using a three lay
ered network which is trained with self-organized clustering and super
vised learning. The network consists of three layers, i.e., a self-org
anized layer, an evaluation layer and an output layer. Since the evalu
ation layer is designed as a simple perceptron network and the output
layer is designed as a fixed weight linear node, the training complexi
ty is the same as a conventional one consisting of self-organized clus
tering and a simple perceptron network. In other words, quite high spe
ed training can be realized. Generally speaking, since the data range
is arbitrary large in signal processing, the network should cover this
range and output a value as accurately as possible. However, it may b
e 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 nod
es, the complexity of each node is reduced and the associated range is
also limited. This results on the higher performance of the network t
han conventional RBFs. This paper introduces a new non-linear spectrum
estimation which consists of LPC analysis and RBF network. it is show
n that accuracy spectrum envelopes can be obtained since a new RBF net
work can estimate some nonlinearities in a speech production.