In a number of electrical engineering problems, so-called “crossing points” – the instants at which two continuous-time signals cross each other – are of interest. Often, particularly in applications using a Digital Signal Processor (DSP), only periodic samples along with a partial statistical characterization of the signals are available. In this situation, we are faced with the following prob...

Orthogonal frequency division multiplexing (OFDM) systems may experience significant inter-carrier interference (ICI) when used in timeand frequency-selective, or doubly-selective, channels. In such cases, the classical symbol estimation schemes, e.g., minimum mean-squared error (MMSE) and zero-forcing (ZF) estimation, require matrix inversion that is prohibitively complex for large symbol leng...

A COMBINED CHANNEL-MODIFIED ADAPTIVE ARRAY MMSE CANCELLER AND VITERBI EQUALIZER by Richard M Friedman In this thesis, a very simple scheme is proposed which couples a maximum-likelihood sequence estimator (MLSE) with a X-element canceller. The method makes use of the MLSE's channel estimator to modify the locally generated training sequence used to calculate the antenna array weights. This meth...

This paper addresses the problem of robust textindependent speaker verification in the presence of missing (masked by noise) features. It presents and assesses several missing feature handling approaches. In these approaches, the speech enhancement and missing feature detection are based on the minimum mean-square error (MMSE) spectral amplitude estimator of Ephraim and Malah [1].

Despite the fact that histograms of speech DFT coefficients are super-Gaussian, not much attention has been paid to develop estimators under these super-Gaussian distributions in combination with perceptual meaningful distortion measures. In this paper we present log-spectral magnitude MMSE estimators under super-Gaussian densities, resulting in an estimator that is perceptually more meaningful...

Uncertainty propagation techniques achieve a more robust automatic speech recognition by modeling the information missing after speech enhancement in the short-time Fourier transform (STFT) domain in probabilistic form. This information is then propagated into the feature domain where recognition takes place and combined with observation uncertainty techniques like uncertainty decoding. In this...

A low-complexity recursive procedure is presented for model selection and minimum mean squared error (MMSE) estimation in linear regression. Emphasis is given to the case of a sparse parameter vector and fewer observations than unknown parameters. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both a set of high posterior probability models and ...

Orthogonal frequency-division multiplexing (OFDM) modulation is a promising technique for achieving the high bit rates required for a wireless multimedia service. Without channel estimation and tracking, OFDM systems have to use differential phase-shift keying (DPSK), which has a 3-dB signalto-noise ratio (SNR) loss compared with coherent phase-shift keying (PSK). To improve the performance of ...

In this paper, we investigate pilot-symbol-aided parameter estimation for orthogonal frequency division multiplexing (OFDM) systems. We first derive a minimum mean-square error (MMSE) pilot-symbol-aided parameter estimator. Then, we discuss a robust implementation of the pilot-symbol-aided estimator that is insensitive to channel statistics. From the simulation results, the required signal-to-n...

Space-time coded orthogonal frequency division multiplexing (OFDM) transmitter diversity techniques have been shown to provide an efficient means of achieving near optimal diversity gain in frequency-selective fading channels. For these systems, knowledge of the channel parameters is required at the receivers for diversity combining and decoding. In this paper, we propose a low complexity, band...