Blind identification of Ambisonic reduced room impulse response

Recently proposed Generalized Time-domain Velocity Vector (GTVV) is a generalization of relative room impulse response in spherical harmonic (aka Ambisonic) domain that allows for blind estimation of early-echo parameters: the directions and relative delays of individual reflections. However, the derived closed-form expression of GTVV mandates few assumptions to hold, most important being that the impulse response of the reference signal needs to be a minimum-phase filter. In practice, the reference is obtained by spatial filtering towards the Direction-of-Arrival of the source, and the aforementioned condition is bounded by the performance of the applied beamformer (and thus, by the Ambisonic array order). In the present work, we suggest to circumvent this problem by properly modelling the GTVV time series, which permits not only to relax the initial assumptions, but also to extract the information therein is a more consistent and efficient manner, entering the realm of blind system identification. Experiments using measured room impulse responses confirm the effectiveness of the proposed approach.Comment: Submitte

A review of cosparse signal recovery methods applied to sound source localization

National audienceThis work aims at comparing several state-of-the-art methods for cosparse signal recovery, in the context of sound source localization. We assess the performance of ve cosparse recovery algorithms: Greedy Analysis Structured Pursuit, l1 and joint l1,2 minimization, Structured Analysis Iterative Hard Thresholding and Structured Analysis Hard Thresholding Pursuit. In addition, we evaluate the performance of these methods against the sparse synthesis paradigm, solved with corresponding joint l1,2 minimization method. For this evaluation, the chosen applicative showcase is sound source localization from simulated measurements of the acoustic pressure eld.L'objectif de cet article est de comparer plusieurs m ethodes de l' etat de l'art pour la reconstruction coparcimonieuse de signaux, dans le contexte de la localisation de sources sonores. Nous evaluons les performances de cinq algorithmes de reconstruction coparcimonieuse : l'algorithme de "Greedy Analysis Structured Pursuit", les minimisations l1 et l1,2 jointe, ainsi que les algorithmes "Structured Analysis Iterative Hard Thresholding" et "Structured Analysis Hard Thresholding Pursuit". Nous comparons egalement ces algorithmes a l'approche de parcimonie a la synth ese, que nous r esolvons par la minimisation jointe l1,2 correspondante. Nous illustrons nos r esultats dans le cadre d'une application a la localisation de sources sonores, r ealise sur des simulations de mesures de champs de pression acoustique

Scattering Features for Multimodal Gait Recognition

International audienceWe consider the problem of identifying people on the basis of their walk (gait) pattern. Classical approaches to tackle this problem are based on, e.g., video recordings or piezoelec-tric sensors embedded in the floor. In this work, we rely on acoustic and vibration measurements, obtained from a microphone and a geophone sensor, respectively. The contribution of this work is twofold. First, we propose a feature extraction method based on an (untrained) shallow scattering network, specially tailored for the gait signals. Second, we demonstrate that fusing the two modalities improves identification in the practically relevant open set scenario

Brain source localization using a physics-driven structured cosparse representation of EEG signals

International audienceLocalizing several potentially synchronous brain activities with low signal-to-noise ratio from ElectroEncephaloGraphic (EEG) recordings is a challenging problem. In this paper we propose a novel source localization method, named CoRE, which uses a Cosparse Representation of EEG signals. The underlying analysis operator is derived from physical laws satisfied by EEG signals, and more particularly from Poisson's equation. In addition, we show how physiological constraints on sources, leading to a given space support and fixed orientations for current dipoles, can be taken into account in the optimization scheme. Computer results, aiming at showing the feasability of the CoRE technique, illustrate its superiority in terms of estimation accuracy over dictionary-based sparse methods and subspace approaches

A-SPADE demo

Tight frame A-SPADE demo: implementation of a tight frame-based sparse analysis version of the SParse Audio DEclipper algorithmThis software allows reproducing experimental results (analysis part only : cf. ASPADE) of the following publications :- S. Kitic, N. Bertin and R. Gribonval. Sparsity and cosparsity for audio declipping: a flexible non-convex approach. In Latent Variable Analysis and Signal Separation, Liberec, 2015.- S. Kitic, N. Bertin and R. Gribonval. Audio Declipping by Cosparse Hard Thresholding. In iTwist – 2nd international – Traveling Workshop on Interactions between Sparse models and Technology, Namur, 2014

Audio Declipping by Cosparse Hard Thresholding

International audienceRecovery of clipped audio signals is a very challenging inverse problem. Recently, it has been successfully addressed by several methods based on the sparse synthesis data model. In this work we propose an algorithm for enhancement of clipped audio signals that exploits the sparse analysis (cosparse) data model. Experiments on real audio data indicate that the algorithm has better signal restoration performance than state-of-the-art sparse synthesis declipping methods