Lucio Bianchi

Compress-then-Analyze versus Analyze-then-Compress: What Is Best in Visual Sensor Networks?

Visual sensor networks (VSNs) have attracted the interest of researchers worldwide in the last few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT). When used to perform visual analysis tasks, VSNs may be operated according to two different paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed and transmitted for further analysis. Conversely, in the analyze-then-compress paradigm, image features are extracted by visual sensor nodes, encoded and then delivered to a remote destination where analysis is performed. The question this paper aims to answer is What is the best visual analysis paradigm in VSNs?To do this, first we empirically characterize the rate-energy-accuracy performance of the two aforementioned paradigms. Then, we leverage such models to formulate a resource allocation problem for VSNs. The problem optimally allocates the specific paradigm used by each camera node in the network and the related transmission source rate, with the objective of optimizing the accuracy of the visual analysis task and the VSN coverage. Experimental results over several VSNs instances demonstrate that there is no “winning” paradigm, but the best performance are obtained by allowing the coexistence of the two and by properly optimizing their utilization.

Deconvolution of plenacoustic images

In this paper we propose a methodology aimed at improving the resolution capabilities of plenacoustic imaging, which is based on deconvolution techniques mutuated from aerospace acoustic imaging. In order to reduce the computational burden, we also propose a modification of the minimization problem that exploits the highly structured information contained in the plenacoustic image. Experiments and simulations show the improvement of the accuracy gained by applying the deconvolution operator.

A plenacoustic approach to acoustic signal extraction

This paper considers the problem of separation of acoustic sources from convolutive mixtures captured by a microphone array. The problem is approached through Plane Wave Decomposition (PWD) of the sound field measured at multiple points along the extension of the array. The directional components of the sound field are analyzed by means of the plenacoustic framework to accurately estimate the direction of arrival of the desired and undesired sources at every point at which the PWD is measured. Multiple spatial filters are designed, one for each PWD measurement point, to leave undistorted the desired source and attenuate the interferer. A successive stage of delay and sum of the outputs of the individual spatial filters enables the reconstruction of the desired source. The use of the plenacoustic framework allows us to gather intuitive and immediate interpretation of the acoustic scene. We prove the effectiveness of the proposed solution through simulations on speech data.

A linear operator for the computation of soundfield maps

In the process of soundfield imaging, as defined in the literature, a microphone array is subdivided into overlapping sub-arrays and soundfield images are obtained by juxtaposition of spatial spectra computed from individual subarray data. In this paper we show that the whole process can be conveniently seen as a linear transformation applied to array data. This linear transformation embeds a nonlinear mapping to cast the directional information in a more convenient domain: the ray space. We show by simulations that the proposed formulation is suitable for fast implementation of the soundfield imaging operation, and, more specifically, for the localization of acoustic sources.

The Ray Space Transform: A New Framework for Wave Field Processing

Soundfield imaging is a special analysis methodology aimed at capturing the directional components of the acoustic field and mapping them onto a domain called “ray space”, where relevant acoustic objects become linear patterns, i.e., sets of collinear points. This allows us to overcome resolution issues while easing far-field assumptions. In this paper, we generalize this concept by introducing the ray space transform for acoustic field representation. The transform is based on a short space-time Fourier transform of the signals captured by a microphone array, using discrete Gabor frames. The resulting transform coefficients are parameterized in the same ray space used for soundfield imaging. The resulting transform enables the definition of analysis and synthesis operators, which exhibit perfect reconstruction capabilities. We show examples of applications of the ray space transform to source localization and spot spatial filtering.

Rendering of directional sources through loudspeaker arrays based on plane wave decomposition

In this paper we present a technique for the rendering of directional sources by means of loudspeaker arrays. The proposed methodology is based on a decomposition of the sound field in terms of plane waves. Within this framework the directivity of the source is naturally included in the rendering problem, therefore accommodating the directivity into the picture becomes much simpler. For this purpose, the loudspeaker array is subdivided into overlapping sub-arrays, each generating a plane wave component. The individual plane waves are then weighed by the desired directivity pattern. Simulations and experimental results show that the proposed technique is able to reproduce the sound field of directional sources with an improved accuracy with respect to existing techniques.

Extraction of Acoustic Sources Through the Processing of Sound Field Maps in the Ray Space

Our goal is to develop a model-based approach to acoustic source extraction from microphone array data, which is suitable for both near-field and far-field sources. A signal representation based on plane-wave (PW) decomposition is suitable for acoustic sources in the far field as the resulting spectrum turns out to be impulsive. When the source approaches the array, however, the curvature of the wavefront causes the spectrum of the PW components to depart from impulsive behavior, thus making source extraction harder to attain. In this paper, we adopt a sound field representation based on the local estimation of the plenacoustic function along the array line. This approach consists of dividing the array into subarrays, and applying the PW analysis on individual subarrays. This has the immediate result of extending the range of validity of the far-field hypothesis, as a source that enters the near-field range of the extended array is still in the far-field range of the subarrays. PW analysis on subarrays allows us to construct the so-called sound field map in a domain of acoustic visibility called ray space. The extraction of the desired source is accomplished through spatial filtering of the sound field map. The design of the spatial filter relies on a linear minimum mean square error criterion defined on the sound field map. The effectiveness of the proposed methodology is proven through an extensive simulation campaign as well as real experiments.

Localization of virtual acoustic sources based on the Hough transform for sound field rendering applications

In this paper we propose a methodology for the localization of virtual acoustic sources for sound field rendering applications. After the reconstruction of the sound field in the listening area by means of circular harmonic decomposition, the virtual source location is found through the Hough transform. We prove the accuracy of the proposed methodology by comparing the source locations estimates with those of a subjective test campaign.

A robust data-independent near-field beamformer for linear microphone arrays

This manuscript presents a robust data-independent near-field beamformer using a linear microphone array. We consider a scenario with a desired sound source and an interferer; our goal in this manuscript is to provide a technique to extract the desired source signal while attenuating the interferer. We formulate the beamformer design problem as a convex optimization problem with additional constraints that aim at controlling the spatial response of the beamformer, with emphasis on the robustness of the approach to errors in the localization of the interferer sound source. We validate our approach through simulations.

High resolution imaging of acoustic reflections with spherical microphone arrays

This paper proposes a methodology for the accurate visualization of acoustic reflections in a room from acoustic measurements by a spherical microphone array. The goal is to provide insight on the relationship between architectural and acoustic features. This task requires high-resolution acoustic images. In this contribution, we achieve this goal by introducing two main modifications of existing approaches. At first, we adopt an explicit model that takes into account the scattering due to the rigid spherical surface where the microphone capsules are hosted. Then, we obtain an estimate of the acoustic power coming from a grid of directions using a spectral analysis approach based on the matching of array data covariance matrix. We conclude this manuscript by showing applications of the devised methodology in real world cases.