Tet Hin Yeap

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Real-time recurrent learning (RTRL), commonly employed for training a fully connected recurrent neural network (RNN), has a drawback of slow convergence rate. In the light of this deficiency, a decision feedback recurrent neural equalizer (DFRNE) using the RTRL requires long training sequences to achieve good performance. In this paper, extended Kalman(More)
— This paper presents an approach to enhance speech feature estimation in the log spectral domain under additive noise environments. A switching linear dynamic model (SLDM) is explored as a parametric model for the clean speech distribution, enforcing a state transition in the feature space and capturing the smooth time evolution of speech conditioned on(More)
In this paper, a Switching Kalman Filter (SKF) with a Generalized Pseudo Bayesian (GPB) algorithm of order 1 is applied to the problem of speech enhancement. It is proposed to use the masking properties of human auditory systems as a perceptual post-filter concatenated with the GPB algorithm. Experiments show that the proposed algorithm can achieve an(More)