Robust equalization of multichannel acoustic systems

In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved by equalizing the channels from the source to microphones. Equalization systems can be computed using estimates of multichannel acoustic impulse responses. However, the estimates obtained from system identification always include errors; the fact that an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this thesis is to propose and investigate robust equalization methods for multichannel acoustic systems in the presence of system identification errors. Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems obtained from these methods are very sensitive to system identification errors. A study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least- squares method is proposed. Channel shortening in connection with the multiple- input/output inverse theorem and the relaxed multichannel least-squares method is discussed. Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization method exploring the use of error models is presented, which incorporates system identification error models in the weighted multichannel least-squares formulation

Semi-blind time-domain equalization for MIMO-OFDM systems

In this paper, a semi-blind time-domain equalization technique is proposed for general multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The received OFDM symbols are shifted by more than or equal to the cyclic prefix (CP) length, and a blind equalizer is designed to completely suppress both intercarrier interference (ICI) and intersymbol interference (ISI) using second-order statistics of the shifted received OFDM symbols. Only a one-tap equalizer is needed to detect the time-domain signals from the blind equalizer output, and one pilot OFDM symbol is utilized to estimate the required channel state information for the design of the one-tap equalizer. The technique is applicable irrespective of whether the CP length is longer than, equal to, or shorter than the channel length. Computer simulations show that the proposed technique outperforms the existing techniques, and it is robust against the number of shifts in excess of the CP length. © 2008 IEEE.published_or_final_versio

Time Domain Signal Detection for MIMO OFDM

The MIMO techniques with OFDM is regarded as a promising solution for increasing data rates, for wireless access qualities of future wireless local area networks, fourth generation wireless communication systems, and for high capacity, as well as better performance. Hence as part of continued research, in this paper an attempt is made to carry out modelling, analysis, channel matrix estimation, synchronization and simulation of MIMO-OFDM system. A time domain signal detection algorithm can be based on Second Order Statistics (SOS) proposed for MIMO-OFDM system over frequency selective fading channels. In this algorithm, an equalizer is first inserted to reduce the MIMO channels to ones with channel length shorter than or equal to the Cyclic Prefix (CP) length. A system model in which the ith received OFDM block left shifted by j samples introduced. MIMO OFDM system model which uses the equalizer can be designed using SOS of the received signal vector to cancel the most of the Inter Symbol Interference (ISI). The transmitted signals are then detected from the equalizer output. In the proposed algorithm, only 2P (P transmitted antennas / users in the MIMO-OFDM system) columns of the channel matrix need to be estimated and channel length estimation is unnecessary, which is an advantage over an existing algorithms. In addition, the proposed algorithm is applicable for irrespective of whether the channel length is shorter than, equal to or longer than the CP length. Simulation results verify the effectiveness of the proposed algorithm and shows that it out performs the existing one in all cases

Low complexity channel shortening and equalization for multi-carrier systems

A new time domain blind adaptive channel shortening algorithm for Discrete Multi Tone (DMT)-based multicarrier systems is first proposed. It is computationally less expensive, and more robust to non- Gaussian impulsive noise environments than a recently reported Sum squared Autocorrelation Minimization (SAM) algorithm. A "left" initialization scheme is also suggested for Carrier Serving Area (CSA) loop Asymmetric Digital Subscriber Line (ADSL) channels. Simulation studies show that by a proper selection of the learning parameter i.e., the step size, the bit rates achieved by the SAM algorithm when operating in an environment contaminated by Additive White Gaussian Noise (AWGN) can be further improved. Next a novel time domain low complexity blind adaptive channel short ening algorithm called Single Lag Autocorrelation Minimization (SLAM) is introduced. The algorithm is totally blind in the sense that it does not require a prior knowledge about the length of the channel impulse response. The proposed novel stopping criterion freezes the adaptation of the SLAM algorithm when the maximum amount of Inter Symbol Interference (ISI) is cancelled. As such, the stopping criterion can also be used with SAM. An attractive alternate frequency domain equalization approach for multicarrier systems is Per Tone Equalization (PTEQ). This scheme en- ables true signal-tonoise ratio optimization to be implemented for each tone and it always achieves higher bit rates than Time domain Equalizer (TEQ) based channel shortening schemes but at the price of increased computational complexity and higher memory requirements. A low complexity (PTEQ) scheme is, therefore, finally proposed. The com plexity of the PTEQ can be traded off with the complexity of the timing synchronization within the system. In particular, it is shown that the use of more than one difference terms and hence a long equalizer in the PTEQ scheme is generally redundant. The PTEQ scheme assumes knowledge of the channel impulse response. In this case synchronization is trivial and it is possible to use only a length two PTEQ equalizer and attain essentially identical bit rate performance to a PTEQ equalizer with length matched to the cyclic prefix. This observation allows for a substantial reduction in computational complexity of the PTEQ scheme in both initialization and data transmission modes. For a reasonable range of values of synchronization error, <5, around the optimal value of 5 = 0, the performance of this length two equalizer is shown to remain relatively constant. For positive synchronization errors, however, the required PTEQ equalizer length is proportional to the synchronization error. A low complexity blind synchronization method is ultimately suggested which is based on the construction of the difference terms of the PTEQ scheme

Semi Blind Time Domain Equalization for MIMO-OFDM Systems

In this thesis, a semi-blind time-domain equalization technique is proposed for general MIMO OFDM systems. The received OFDM symbols are shifted by more than or equal to the cyclic prefix (CP) length, and a blind equalizer is designed to completely suppress both inter-carrier interference (ICI) and inter-symbol interference (ISI) using second-order statistics of the shifted received OFDM symbols. Only a one-tap equalizer is needed to detect the time domain signals from the blind equalizer output, and one pilot OFDM symbol is utilized to estimate the required channel state information for the design of the one-tap equalizer. Simulation results show that this technique is robust against the number of shifts in excess of the CP length

High-resolution diffusion-weighted brain MRI under motion

Magnetic resonance imaging is one of the fastest developing medical imaging techniques. It provides excellent soft tissue contrast and has been a leading tool for neuroradiology and neuroscience research over the last decades. One of the possible MR imaging contrasts is the ability to visualize diffusion processes. The method, referred to as diffusion-weighted imaging, is one of the most common clinical contrasts but is prone to artifacts and is challenging to acquire at high resolutions. This thesis aimed to improve the resolution of diffusion weighted imaging, both in a clinical and in a research context. While diffusion-weighted imaging traditionally has been considered a 2D technique the manuscripts and methods presented here explore 3D diffusion acquisitions with isotropic resolution. Acquiring multiple small 3D volumes, or slabs, which are combined into one full volume has been the method of choice in this work. The first paper presented explores a parallel imaging driven multi-echo EPI readout to enable high resolution with reduced geometric distortions. The work performed on diffusion phase correction lead to an understanding that was used for the subsequent multi-slab papers. The second and third papers introduce the diffusion-weighted 3D multi-slab echo-planar imaging technique and explore its advantages and performance. As the method requires a slightly increased acquisition time the need for prospective motion correction became apparent. The forth paper suggests a new motion navigator using the subcutaneous fat surrounding the skull for rigid body head motion estimation, dubbed FatNav. The spatially sparse representation of the fat signal allowed for high parallel imaging acceleration factors, short acquisition times, and reduced geometric distortions of the navigator. The fifth manuscript presents a combination of the high-resolution 3D multi-slab technique and a modified FatNav module. Unlike our first FatNav implementation, using a single sagittal slab, this modified navigator acquired orthogonal projections of the head using the fat signal alone. The combined use of both presented methods provides a promising start for a fully motion corrected high-resolution diffusion acquisition in a clinical setting

Polynomial matrix eigenvalue decomposition techniques for multichannel signal processing

Polynomial eigenvalue decomposition (PEVD) is an extension of the eigenvalue decomposition (EVD) for para-Hermitian polynomial matrices, and it has been shown to be a powerful tool for broadband extensions of narrowband signal processing problems. In the context of broadband sensor arrays, the PEVD allows the para-Hermitian matrix that results from the calculation of a space-time covariance matrix of the convolutively mixed signals to be diagonalised. Once the matrix is diagonalised, not only can the correlation between different sensor signals be removed but the signal and noise subspaces can also be identified. This process is referred to as broadband subspace decomposition, and it plays a very important role in many areas that require signal separation techniques for multichannel convolutive mixtures, such as speech recognition, radar clutter suppression, underwater acoustics, etc. The multiple shift second order sequential best rotation (MS-SBR2) algorithm, built on the most established SBR2 algorithm, is proposed to compute the PEVD of para-Hermitian matrices. By annihilating multiple off-diagonal elements per iteration, the MS-SBR2 algorithm shows a potential advantage over its predecessor (SBR2) in terms of the computational speed. Furthermore, the MS-SBR2 algorithm permits us to minimise the order growth of polynomial matrices by shifting rows (or columns) in the same direction across iterations, which can potentially reduce the computational load of the algorithm. The effectiveness of the proposed MS-SBR2 algorithm is demonstrated by various para-Hermitian matrix examples, including randomly generated matrices with different sizes and matrices generated from source models with different dynamic ranges and relations between the sources’ power spectral densities. A worked example is presented to demonstrate how the MS-SBR2 algorithm can be used to strongly decorrelate a set of convolutively mixed signals. Furthermore, the performance metrics and computational complexity of MS-SBR2 are analysed and compared to other existing PEVD algorithms by means of numerical examples. Finally, two potential applications of theMS-SBR2 algorithm, includingmultichannel spectral factorisation and decoupling of broadband multiple-input multiple-output (MIMO) systems, are demonstrated in this dissertation

Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Online Speaker Separation Using Deep Clustering

In this thesis, a low-latency variant of speaker-independent deep clustering method is proposed for speaker separation. Compared to the offline deep clustering separation system, bidirectional long-short term memory networks (BLSTMs) are replaced with long-short term memory networks (LSTMs). The reason is that the data has to be fed to the BLSTM networks both forward and backward directions. Additionally, the final outputs depend on both directions, which make online processing not possible. Also, 32 ms synthesis window is replaced with 8 ms in order to cooperate with low- latency applications like hearing aids since the algorithmic latency depends upon the length of synthesis window. Furthermore, the beginning of the audio mixture, here, referred as buffer, is used to get the cluster centers for the constituent speakers in the mixture serving as the initialization purpose. Later, those centers are used to assign clusters for the rest of the mixture to achieve speaker separation with the latency of 8 ms. The algorithm is evaluated on the Wall Street Journal corpus (WSJ0). Changing the networks from BLSTM to LSTM while keeping the same window length degrades the separation performance measured by signal-to-distortion ratio (SDR) by 1.0 dB, which implies that the future information is important for the separation. For investigating the effect of window length, keeping the same network structure (LSTM), by changing window length from 32 ms to 8 ms, another 1.1 dB drop in SDR is found. For the low-latency deep clustering speaker separation system, different duration of buffer is studied. It is observed that initially, the separation performance increases as the buffer increases. However, with buffer length of 0.3 s, the separation performance keeps steady even by increasing the buffer. Compared to offline deep clustering separation system, degradation of 2.8 dB in SDR is observed for online system