Localisation de sources acoustiques en milieu réverbérant par déréverbération semi-aveugle

La localisation de sources à bande étroites dans les milieux réverbérants est un problème difficile à cause multiples réflexions du signal direct sur les murs. Récemment, nous avons développé des méthodes pour localiser des sources monochromatiques au sein d'une antenne de quelques dizaines de microphones. Pour cela, un modèle de déréverbération permet de séparer le champ acoustique mesuré en deux composantes : le signal direct issu de la source et la réverbération. Cette étape de déréverbération est effectuée par la projection des mesures sur une base de fonctions théoriques capturant l'énergie due à la réverbération, l'étape de localisation s'effectuant à l'aide d'algorithmes type beamforming. Cependant, cette méthode nécessite en contrepartie un nombre élevé de mesures. Nous montrons que le projecteur obtenu à partir des fonctions théoriques peut aussi être construit expérimentalement par l'apprentissage et l'inter-corrélation de quelques réponses impulsionnelles du milieu. Ainsi, il est possible de prendre implicitement en compte les propriétés physiques de l'environnement, d'améliorer l'étape de déréverbération et de diminuer le nombre de mesures. Dans le cas de milieux faiblement hétérogènes, cette étape de calibration semi-aveugle permet également de supprimer une partie de la contribution des hétérogénéités et de localiser les sources. La méthode est étudiée numériquement puis validée expérimentalement dans une grande salle avec une antenne d'une centaine de microphones

Spatial and Temporal Compressive Sensing for Vibration-based Monitoring: Fundamental Studies with Beam Vibrations

Vibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high data volume and require powering sensors for prolonged duration. Furthermore, adequate spatial resolution, typically involves instrumenting structures with a large array of sensors. This research shows that applying Compressive Sensing (CS) can significantly reduce both the volume of data and number of sensors in vibration monitoring applications. Random sampling and the inherent sparsity of vibration signals in the frequency domain enables this reduction. Additionally, by exploiting the sparsity of mode shapes, CS can also enable efficient spatial reconstruction using fewer spatially distributed sensors than a traditional approach. CS can thereby reduce the cost and power requirement of sensing as well as streamline data storage and processing in monitoring applications. In well-instrumented structures, CS can enable continuous monitoring in case of sensor or computational failures. The scope of this research was to establish CS as a viable method for SHM with application to beam vibrations. Finite element based simulations demonstrated CS-based frequency recovery from free vibration response of simply supported, fixed-fixed and cantilever beams. Specifically, CS was used to detect shift in natural frequencies of vibration due to structural change using considerably less data than required by traditional sampling. Experimental results using a cantilever beam provided further insight into this approach. In the experimental study, impulse response of the beam was used to recover natural frequencies of vibration with CS. It was shown that CS could discern changes in natural frequencies under modified beam parameters. When the basis functions were modified to accommodate the effect of damping, the performance of CS-based recovery further improved. Effect of noise in CS-based frequency recovery was also studied. In addition to incorporating damping, formulating noise-handling as a part of the CS algorithm for beam vibrations facilitated detecting shift in frequencies from even fewer samples. In the spatial domain, CS was primarily developed to focus on image processing applications, where the signals and basis functions are very different from those required for mechanical beam vibrations. Therefore, it mandated reformulation of the CS problem that would handle related challenges and enable the reconstruction of spatial beam response using very few sensor data. Specifically, this research addresses CS-based reconstruction of deflection shape of beams with fixed boundary conditions. Presence of a fixed end makes hyperbolic terms indispensable in the basis, which in turn causes numerical inconsistencies. Two approaches are discussed to mitigate this problem. The first approach is to restrict the hyperbolic terms in the basis to lower frequencies to ensure well conditioning. The second, a more systematic approach, is to generate an augmented basis function that will combine harmonic and hyperbolic terms. At higher frequencies, the combined hyperbolic terms will limit each other\u27s magnitude, thus ensuring boundedness. This research thus lays the foundation for formulating the CS problem for the field of mechanical vibrations. It presents fundamental studies and discusses open-ended challenges while implementing CS to this field that will pave way for further research

A blind dereverberation method for narrowband source localization

International audienceNarrowband source localization gets extremely challenging in strong reverberation. When the room is perfectly known, some dictionary-based methods have recently been proposed , allowing source localization with few measurements. In this paper, we first show that, for these methods, the choice of frequencies is important as they fail to localize sources that emit at a frequency near the modal frequencies of the room. A more difficult case, but also important in practice, is when the room geometry and boundary conditions are unknown. In this setup, we introduce a new model for the acoustic soundfield, based on the Vekua theory, that allows a separation of the field into its reverberant and direct source contributions, at the cost of more measurements. This can be used for the design of a dereverberation pre-processing step, suitable for a large variety of standard source localization techniques. We discuss the spatial sampling strategies for the sound field, in order to successfully recover acoustic sources, and the influence of parameters such as number of measurements and model order. This is validated in numerical and experimental tests, that show that this method significantly improves localization in strong reverberant conditions