In this work, we are concerned with optimal estimation of clean speech from its noisy version based on a speech model we propose. We first propose a (single) speech model which satisfactorily describes voiced and unvoiced speech and silence (i.e., pauses between speech utterances), and also allows for exploitation of the long term characteristics of noise. We then reformulate the model equations so as to facilitate subsequent application of the well-established Kalman filter for computing the optimal estimate of the clean speech in the minimum-mean-square-error sense. Since the standard algorithm for Kalman-filtering involves multiplications of very large matrices and thus demands high computational cost, we devise a mathematically equivalent algorithm which is computationally much more efficient, by exploiting the sparsity of the matrices concerned. Next, we present the methods we use for estimating the model parameters and give a complete description of the enhancement process. Performance assessment based on spectrogram plots, objective measures and informal subjective listening tests all indicate that our method gives consistently good results. As far as signal-to-noise ratio is concerned, the improvements over existing methods can be as high as 4 dB.

In this work, we attempt to refine the methods based on autoregressive (AR) modeling for speech enhancement (Paliwal and Basu, 1987); Gibson et al., 1991). As a matter of fact, AR modelling, which is a key strategy of the two above methods, is known to be good for representing unvoiced speech but not quite appropriate for voiced speech which is quite periodic in nature. Here, we incorporate a speech model which satisfactorily describes voiced and unvoiced speeches and silence (i.e., pauses between speech utterances) into the enhancement framework developed in the two above methods, and specifically devise an algorithm for computing the optimal estimate of the clean speech in the minimum-mean-square-error sense. We also present the methods we use for estimating the model parameters and give a description of the complete enhancement procedure. Performance assessment based on spectrogram plots, objective measures and informal subjective listening tests all indicate that our method gives consistently good results.

We develop a minimum-noise-variance beamformer employing one electromagnetic vector sensor, capable of measuring the complete electric and magnetic fields induced by electromagnetic signals at one point. Two types of signals are considered: one carries a single message, and the other carries two independent messages simultaneously. The state of polarization of the interference under consideration ranges from completely polarized to unpolarized. To analyze the performance, we first obtain explicit expressions for the signal to interference-plus-noise ratio (SINR) in terms of the parameters of the desired signal, interference and noise. Then we discuss some physical implications associated with the SINR expressions. Our SINR expressions provide a basis for effective interference suppression, as well as generation of dual-message signals of which the two message signals have minimum interference effect on one another.

We investigate whether musical noise, which often exists in speech enhanced using spectral subtraction, can be suppressed. Via exploiting some specific characteristics of human speech, we propose a method that can effectively suppress musical noise without a noticeable effect on speech intelligibility. Performance assessments confirm that our method is effective.

We develop a high-resolution method for estimating the directions-of-arrival of partially polarised signals with electromagnetic (EM) vector sensors, of which each provides measurements of the complete electric and magnetic fields induced by EM signals. The method is computationally efficient since unlike many high-resolution methods, it does not involve searching across a multi-dimensional array manifold. In addition, it does not require a priori information about the array system such as sensor positions.