Sketching for nearfield acoustic imaging of heavy-tailed sources

International audienceWe propose a probabilistic model for acoustic source localization with known but arbitrary geometry of the microphone array. The approach has several features. First, it relies on a simple nearfield acoustic model for wave propagation. Second, it does not require the number of active sources. On the contrary, it produces a heat map representing the energy of a large set of candidate locations, thus imaging the acoustic field. Second, it relies on a heavy-tail alpha-stable probabilistic model, whose most important feature is to yield an estimation strategy where the multichannel signals need to be processed only once in a simple on- line procedure, called sketching. This sketching produces a fixed-sized representation of the data that is then analyzed for localization. The resulting algorithm has a small computational complexity and in this paper, we demonstrate that it compares favorably with state of the art for localization in realistic simulations of reverberant environments

Alpha-stable low-rank plus residual decomposition for speech enhancement

International audienceIn this study, we propose a novel probabilistic model for separating clean speech signals from noisy mixtures by decomposing the mixture spectrograms into a structured speech part and a more flexible residual part. The main novelty in our model is that it uses a family of heavy-tailed distributions, so called the α-stable distributions, for modeling the residual signal. We develop an expectation-maximization algorithm for parameter estimation and a Monte Carlo scheme for posterior estimation of the clean speech. Our experiments show that the proposed method outperforms relevant factorization-based algorithms by a significant margin

Scalable Source Localization with Multichannel Alpha-Stable Distributions

International audienceIn this paper, we focus on the problem of sound source localization and we propose a technique that exploits the known and arbitrary geometry of the microphone array. While most probabilistic techniques presented in the past rely on Gaussian models, we go further in this direction and detail a method for source localization that is based on the recently proposed alpha-stable harmonizable processes. They include Cauchy and Gaussian as special cases and their remarkable feature is to allow a simple modeling of impulsive and real world sounds with few parameters. The approach we present builds on the classical convolutive mixing model and has the particularities of requiring going through the data only once, to also work in the underdetermined case of more sources than microphones and to allow massively parallelizable implementations operating in the time-frequency domain. We show that the method yields interesting performance for acoustic imaging in realistic simulations