Temporal Decorrelation Using Teacher Forcing Anti-Hebbian Learning and Its Application In Adaptive Blind Source Separation
نویسندگان
چکیده
Abstract: This paper proposes a network architecture to compute on-line the temporal crosscorrelation function between two signals, either stationary or locally stationary. We show that the weights of a multi-FIR (Finite Impulse Response) filter trained with a teacher forcing anti-Hebbian rule encode the crosscorrelation function between the input and the desired response. We extend this network to the Gamma filter which is an IIR (Infinite Impulse Response) filter and also to nonlinear filters. This temporal correlation idea is applied to the blind source separation problem. From these networks we build a recurrent system trained on-line with anti-Hebbian learning which performs temporal decorrelation on the mixed signals. The system performance is tested in speech signals mixed in time with good results. A comparison of the performance among the different topologies will also be presented in the final paper.
منابع مشابه
Blind Signal Deconvolution by Spatio Temporal Decorrelation and Demixing
In this paper we present a simple efficient local unsupervised learning algorithm for on-line adaptive multichannel blind deconvolution and separation of i.i.d. sources. Under mild conditions, there exits a stable inverse system so that the source signals can be exactly recovered from their convolutive mixtures. Based on the existence of the inverse filter, we construct a two-stage neural netwo...
متن کاملBlind Equalization of Simo Channels via Spatio Temporal Anti Hebbian Learning Rule
This paper presents a new distributed processing ap proach to direct blind equalization of Single Input Multiple Out put SIMO channels Under mild conditions it is shown here that we can recover the original source signal up to its scaled and delayed version by decorrelating the equalizer neural network outputs in spatio temporal domain Spatio temporal anti Hebbian learn ing rule simple local bi...
متن کاملLoss function for blind source separation-minimum entropy criterion and its generalized anti-Hebbian rules
Blind source separation has been intriguing many scientists. In adaptive signal processing, LMS (kast-mean squared) algorithm has long been used in signal enhancement and noise cancellation but it cannot ovexome the d$jiculty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a ...
متن کاملNoise reduction and speech enhancement via temporal anti-Hebbian learning
Temporal extensions of both linear and nonlinear anti-Hebbian learning have been shown to be suited to the problem of blind separation of sources from their convolved mixtures. This paper presents a generalized form of anti-Hebbian learning for a partially connected recurrent network based on the maximum likelihood estimation principle. Inspired by features of the binaural unmasking effect the ...
متن کاملBlind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation
We propose two methods that reduce the post-nonlinear blind source separation problem (PNLBSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm—a powerful technique from nonparametric statistics—to approximately invert the componentwise nonlinear functions. The second method is a Gaussianiz...
متن کامل