Relevant Deconvolution For Acoustic Source Estimation

We describe a robust deconvolution algorithm for simultaneously estimating an acoustic source signal and convolutive filters associated with the acoustic room impulse responses from a pair of microphone signals. In contrast to conventional blind deconvolution techniques which rely upon a knowledge of the statistics of the source signal, our algorithm exploits the nonnegativity and sparsity structure of room impulse responses. The algorithm is formulated as a quadratic optimization problem with respect to both the source signal and filter coefficients, and proceeds by iteratively solving the optimization in two alternating steps. In the H-step, the nonnegative filter coefficients are optimally estimated within a Bayesian framework using a relevant set of regularization parameters. In the S-step, the source signal is estimated without any prior assumption on its statistical distribution. The resulting estimates converge to a relevant solution exhibiting appropriate sparseness in the filters. Simulation results indicate that the algorithm is able to precisely recover both the source signal and filter coefficients, even in the presence of large ambient noise

Bayesian \u3cem\u3eL\u3c/em\u3e\u3csub\u3e1\u3c/sub\u3e-Norm Sparse Learning

We propose a Bayesian framework for learning the optimal regularization parameter in the L1-norm penalized least-mean-square (LMS) problem, also known as LASSO [1] or basis pursuit [2]. The setting of the regularization parameter is critical for deriving a correct solution. In most existing methods, the scalar regularization parameter is often determined in a heuristic manner; in contrast, our approach infers the optimal regularization setting under a Bayesian framework. Furthermore, Bayesian inference enables an independent regularization scheme where each coefficient (or weight) is associated with an independent regularization parameter. Simulations illustrate the improvement using our method in discovering sparse structure from noisy data

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Blind Sparse-nonnegative (BSN) Channel Identification for Acousitic Time-Difference-of-Arrival Estimation

Estimating time-difference-of-arrival (TDOA) remains a challenging task when acoustic environments are reverberant and noisy. Blind channel identification approaches for TDOA estimation explicitly model multipath reflections and have been demonstrated to be effective in dealing with reverberation. Unfortunately, existing blind channel identification algorithms are sensitive to ambient noise. This papers hows how to resolve the noise sensitivity issue by exploiting prior knowledge about an acoustic room impulse response (RIR), namely, an acoustic RIR can be modeled by a sparse-nonnegative FIR filter. This paper shows how to formulate a single-input two-output blind channel identification into a least square convex optimization, and how to incorporate the sparsity and nonnegativity priors so that the resulting optimization remains convex and can be solved efficiently. The proposed blind sparse-nonnegative (BSN) channel identification approach for TDOA estimation is not only robust to reverberation, but also robust to ambient noise, as demonstrated by simulations and experiments in real acoustic environments

Cramér-Rao sensitivity limits for astronomical instruments: implications for interferometer design

Multiple-telescope interferometry for high-angular-resolution astronomical imaging in the optical–IR–far-IR bands is currently a topic of great scientific interest. The fundamentals that govern the sensitivity of direct-detection instruments and interferometers are reviewed, and the rigorous sensitivity limits imposed by the Cramér–Rao theorem are discussed. Numerical calculations of the Cramér–Rao limit are carried out for a simple example, and the results are used to support the argument that interferometers that have more compact instantaneous beam patterns are more sensitive, since they extract more spatial information from each detected photon. This argument favors arrays with a larger number of telescopes, and it favors all-on-one beam-combining methods as compared with pairwise combination

Single-Channel Signal Separation Using Spectral Basis Correlation with Sparse Nonnegative Tensor Factorization

A novel approach for solving the single-channel signal separation is presented the proposed sparse nonnegative tensor factorization under the framework of maximum a posteriori probability and adaptively fine-tuned using the hierarchical Bayesian approach with a new mixing mixture model. The mixing mixture is an analogy of a stereo signal concept given by one real and the other virtual microphones. An “imitated-stereo” mixture model is thus developed by weighting and time-shifting the original single-channel mixture. This leads to an artificial mixing system of dual channels which gives rise to a new form of spectral basis correlation diversity of the sources. Underlying all factorization algorithms is the principal difficulty in estimating the adequate number of latent components for each signal. This paper addresses these issues by developing a framework for pruning unnecessary components and incorporating a modified multivariate rectified Gaussian prior information into the spectral basis features. The parameters of the imitated-stereo model are estimated via the proposed sparse nonnegative tensor factorization with Itakura–Saito divergence. In addition, the separability conditions of the proposed mixture model are derived and demonstrated that the proposed method can separate real-time captured mixtures. Experimental testing on real audio sources has been conducted to verify the capability of the proposed method