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One of the important aspects of the mobile communication industry is satisfying customers' needs most economically. Indeed, customers expect good and consistent quality of service from the provider. In mobile telephony, this amounts to controlling the speech quality as “perceived” by the customers. Controlling the perceptual speech quality necessitates reliable measurement of the quality first, followed by exercising direct control of it. The end-users' quality has traditionally been monitored and controlled based on radio link measurements such as the Signal Interference Ratio (SIR), Bit Error Rate (BER), or Frame Error Rate (FER). The ultimate measure of perceived speech quality is realized through subjective listening tests, but this is not practical for real-time day to day applications. In recent years, objective quality measurement algorithms have been developed to predict the subjective quality with considerable accuracy. The state of art in the International Telecommunication Union's Telecommunication Standardization sector (ITU-T) recommendation for referenced objective quality measurement method is P.862 Perceptual Evaluation of Speech Quality (PESQ) model. These algorithms have yet to be applied for end user quality control in cellular networks using Statistical Process Control (SPC). In this paper, the application of Cumulative Sum (CUSUM) scheme, based on the PESQ algorithm for perceptual speech quality control is presented.

This paper proposes a design of digital waveform sets, which can be used as transmitter signature waveforms for multidimensional signaling and/or multiuser communications. The aim is to design a set of quasiorthogonal digital waveforms with prescribed specifications in time and frequency domains, optimized with respect to the given channels. It will be shown that the digital waveform set design problem can be formulated as a constrained nonlinear optimization problem. For bandwidth efficiency, all the digital waveforms in the set are designed in such a way that, the overall frequency response from each transmitter to the corresponding receiver has approximately the same desired spectral shape in a prescribed passband; To minimize intersymbol interference and cochannel interference, all the digital waveforms in the set are constrained to have low values of weighted (by the channel) autocorrelation at nonzero translates of multiple symbol interval and low values of weighted (by the channel) crosscorrelation at all translates of multiple symbol interval. Numerical results are presented to demonstrate the effectiveness of the proposed design.

This paper presents a new normalized frequency domain approach for adaptive blind equalization for multiple-input multiple-output (MIMO) communication systems. We first develop a time domain block based algorithm that updates the equalizer coefficients once per block of data symbols. As the time domain algorithm involves infinite summations in the separation cost function, an approximation is then proposed to reduce the cost function to finite summations. As a consequence, the block based time domain algorithm can be implemented in the frequency domain to reduce the computational complexity associated with the conventional symbol-by-symbol time domain update. Furthermore, by recognizing that the signals in the frequency domain are orthogonal, it is possible to significantly improve the convergence rate by normalizing the update equations with respect to the signal power in each frequency bin. Simulation results show that the proposed algorithm can successfully equalize the received signals while maintaining low computational complexity. Moreover, the presented normalized frequency domain blind equalization algorithm for MIMO systems significantly improves the convergence rate

The multi-rate subband equaliser converts the computationally complex fullband equaliser structure into several parallel low complexity subband equalisers, each operating independently. The fullband equaliser has a minimum mean square error (MMSE) dependent upon the filter coefficients and the relative delay between the desired symbol and received signal. The subband equalisers MMSE performance is also dependent upon the subband adaptive filter (SAF) coefficients and the relative delay between the desired and received subband signal components, referred herein as the alignment delay. We propose a new structure allowing implementation of independent alignment delays for each subband to achieve the MMSE with respect to both the SAF coefficients and the alignment delay. Simulation results show that the use of independent alignment delays in each subband provides significant improvement over the standard subband transversal equaliser.

In this paper, frame error rate (FER) performance of enhanced verification-based decoding algorithm (EVA) is investigated for packet-based low-density parity-check (LDPC) codes over binary symmetric channel (BSC). By taking the false verification into account, a recursive statistical model is proposed to analyze the FER performance based on the packet-level analysis. Simulation results demonstrate that the proposed model works for various packet sizes and channel parameters of practical interest with different verification constraints.

This paper introduces a new constrained adaptive subband beamformer algorithm for speech enhancement in acoustic telecommunication systems. The solution relies on a pre-calculated source covariance matrix and recursive estimates of background noise- and handsfree signal covariance matrices. The constraint acts as an eye-opening in a vicinity of the near-field location of the source and degradations from steering-vector errors can therefor be made small. The algorithm is applied in subbands using a uniform multi channel over-sampled filterbank. Simulations with real speech recorded in an automobile hands-free environment show 19 dB noise reduction and 20 dB hands-free suppression.

The problem of finding the Weiner filters for a wideband adaptive beamformer is treated. Explicit expressions are given for the filters and the error power spectrum in the frequency domain. The expressions are simple to program and make it possible to investigate superresolution for wideband signals and to determine sufficient lengths of finite impulse response filters in adaptive arrays. The Griffiths-Jim (1982) structure implies that the ordinary LMS algorithm can be used without constraints. >

Very long adaptive filters are used for controlling the acoustic echo due to the acoustic echo of the loudspeaker and microphone. Subband adaptive filters have been proposed to save computations as well as speed up convergence. Delayless subband adaptive filters remove the additional signal path delay due to the analysis-synthesis system in their conventional counterpart by transforming the subband filters to a corresponding fullband one and cancelling the echo using the fullband filter. The weight transform from subband to fullband plays a crucial role in the delayless subband adaptive filters. In this paper, a defect of the FFT-stacking weight transform is identified, and new transform schemes are proposed. Simulation results show that significant performance improvements can be achieved by employing the proposed weight transform schemes.

Considerable attention has been devoted to the reverberant blind source separation problem: in particular, the concept of time-frequency masking. However, realistic acoustic scenarios often comprise not only reverberation, but also additive noise due to factors such as non-ideal channels. This paper presents robust evaluations of a time-frequency masking approach for separation in such realistic conditions. The fuzzy c-means clustering algorithm is used to cluster spatial feature cues into a time-frequency mask. Experimental results demonstrated superiority in separation, with notable improvements in the SNR additionally observed. Not only does this establish the proposed scheme viable for reverberant blind source separation, but also as a credible means of speech enhancement in the presence of additive broadband noise.

This paper presents a new blind speech separation algorithm using beamforming technique that is capable of extracting each individual speech signal from a mixture of three speech sources in a room. The speech separation algorithm utilizes the steered response power phase transform for obtaining a localization estimate for each individual speech source in the frequency domain. Based on those estimates each desired speech signal is extracted from the speech mixture using an optimal beamforming technique. To solve the permutation problem, a permutation alignment algorithm based on the mutual output correlation is employed to group the output signals into the correct sources from each frequency bin. Evaluations using real speech recordings in a room environment show that the proposed blind speech separation algorithm offers high interference suppression level whilst maintaining low distortion level for each desired signal.

This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assumption in modeling speaker specific characteristics via Gaussian mixture models. This paper proposes the use of full covariance models that can capture linear correlations among feature components. This is of importance for missing data marginalization techniques as they depend on spectral rather than cepstral feature representations. Bounded and complete marginalization schemes are investigated both with diagonal and full covariance mixture models. Speaker identification experiments using stationary and non-stationary noise confirm that full covariance models are indeed superior compared to diagonal models.

Broadband wireless systems require powerful equalizers which can cope with long channel impulse responses and severe intersymbol interference (ISI). In this paper, a frequency domain modified constant modulus algorithm (PD-MCMA) is presented. The employed modified CMA (MCMA) is chosen to confine the phase ambiguity of the conventional CMA to a 90/spl deg/ phase shift solution. On this foundation, a block MCMA algorithm which updates the equalizer coefficients on a block-by-block basis is proposed. The advantage of this block MCMA is that it can be equivalently implemented in the frequency domain. This approach offers a large reduction in computational complexity compared to time domain algorithms. For an equalizer with 256 coefficients, the number of real multiplications required by the FD-MCMA is only 9.5% of that required by the MCMA. Furthermore, convergence of the proposed FD-MCMA is significantly improved by properly normalizing the update equations. Simulations show that the normalized FD-MCMA converges much faster than the MCMA and the FD-MCMA while maintaining approximately the same bit error rate (BER) performance.