Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings

We tackle the multi-party speech recovery problem through modeling the acoustic of the reverberant chambers. Our approach exploits structured sparsity models to perform room modeling and speech recovery. We propose a scheme for characterizing the room acoustic from the unknown competing speech sources relying on localization of the early images of the speakers by sparse approximation of the spatial spectra of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings demonstrate the effectiveness of the proposed approach for multi-party speech recovery and recognition.Comment: 31 page

Blind Demixing for Low-Latency Communication

In the next generation wireless networks, lowlatency communication is critical to support emerging diversified applications, e.g., Tactile Internet and Virtual Reality. In this paper, a novel blind demixing approach is developed to reduce the channel signaling overhead, thereby supporting low-latency communication. Specifically, we develop a low-rank approach to recover the original information only based on a single observed vector without any channel estimation. Unfortunately, this problem turns out to be a highly intractable non-convex optimization problem due to the multiple non-convex rankone constraints. To address the unique challenges, the quotient manifold geometry of product of complex asymmetric rankone matrices is exploited by equivalently reformulating original complex asymmetric matrices to the Hermitian positive semidefinite matrices. We further generalize the geometric concepts of the complex product manifolds via element-wise extension of the geometric concepts of the individual manifolds. A scalable Riemannian trust-region algorithm is then developed to solve the blind demixing problem efficiently with fast convergence rates and low iteration cost. Numerical results will demonstrate the algorithmic advantages and admirable performance of the proposed algorithm compared with the state-of-art methods.Comment: 14 pages, accepted by IEEE Transaction on Wireless Communicatio

Signal Processing Design of Low Probability of Intercept Waveforms

This thesis investigates a modification to Differential Phase Shift Keyed (DPSK) modulation to create a Low Probability of Interception/Exploitation (LPI/LPE) communications signal. A pseudorandom timing offset is applied to each symbol in the communications stream to intentionally create intersymbol interference (ISI) that hinders accurate symbol estimation and bit sequence recovery by a non-cooperative receiver. Two cooperative receiver strategies are proposed to mitigate the ISI due to symbol timing offset: a modified minimum Mean Square Error (MMSE) equalization algorithm and a multiplexed bank of equalizer filters determined by an adaptive Least Mean Square (LMS) algorithm. Both cooperative receivers require some knowledge of the pseudorandom symbol timing dither to successfully demodulate the communications waveform. Numerical Matlab® simulation is used to demonstrate the bit error rate performance of cooperative receivers and notional non-cooperative receivers for binary, 4-ary, and 8-ary DPSK waveforms transmitted through a line-of-sight, additive white Gaussian noise channel. Simulation results suggest that proper selection of pulse shape and probability distribution of symbol timing offsets produces a waveform that is accurately demodulated by the proposed cooperative receivers and significantly degrades non-cooperative receiver symbol estimation accuracy. In typical simulations, non-cooperative receivers required 2-8 dB more signal power than cooperative receivers to achieve a bit error rate of 1.0%. For nearly all reasonable parameter selections, non-cooperative receivers produced bit error rates in excess of 0.1%, even when signal power is unconstrained

Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

Radio astronomical imaging in the presence of strong radio interference

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio astronomical imaging. Current deconvolution procedures such as CLEAN are shown to be unsuitable to spatially filtered data, and the necessary corrections are derived. To that end, we reformulate the imaging (deconvolution/calibration) process as a sequential estimation of the locations of astronomical sources. This not only leads to an extended CLEAN algorithm, the formulation also allows to insert other array signal processing techniques for direction finding, and gives estimates of the expected image quality and the amount of interference suppression that can be achieved. Finally, a maximum likelihood procedure for the imaging is derived, and an approximate ML image formation technique is proposed to overcome the computational burden involved. Some of the effects of the new algorithms are shown in simulated images. Keywords: Radio astronomy, synthesis imaging, parametric imaging, interference mitigation, spatial filtering, maximum likelihood, minimum variance, CLEAN.Comment: 27 pages, 7 figures. Paper with higher resolution color figures at http://cobalt.et.tudelft.nl/~leshem/postscripts/leshem/imaging.ps.g

Detection Strategies and Intercept Metrics for Intra-Pulse Radar-Embedded Communications

This thesis presents various detection strategies and intercept metrics to evaluate and design an intra-pulse radar-embedded communication system. This system embeds covert communication symbols in masking interference provided by the reflections of a pulsed radar emission. This thesis considers the case where the communicating device is a transponder or tag present in an area that is illuminated by a radar. The radar is considered to be the communication receiver. As with any communication system, performance (as measured by reliability and data rate) should be maximized between the tag and radar. However, unlike conventional communication systems, the symbols here should also have a low-probability of intercept (LPI). This thesis examines the trade-offs associated with the design of a practical radar-embedded communication system. A diagonally-loaded decorrelating receiver is developed and enhanced with a second stage based on the Neyman-Pearson criterion. For a practical system, the communication symbols will likely encounter multipath. The tag may then use a pre-distortion strategy known as time-reversal to improve the signal-to-noise ratio at the radar receiver thereby enhancing communication performance. The development of several intercept metrics are shown and the logic behind the design evolutions are explained. A formal analysis of the processing gain by the desired receiver relative to the intercept receivers is given. Finally, simulations are shown for all cases, to validate the design metrics