Room reconstruction from sound is the problem of estimating indoor space boundaries given only a set of sound events acquired by an array of microphones. In order to find a solution in realistic scenarios, it is necessary a robust and practical method that can solve the echo labelling problem, i.e. assigning at each signal delay the correct reflector that has generated it. Although being an NP-hard problem, in this paper we demonstrate that it is possible to solve the echo labelling problem in a reasonable computational time without the need of additional hypotheses on the echoes order of arrival.

A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed. A likelihood function is derived from this map and fed to a particle filter yielding the target location estimation on the acoustic image. The method is tested on two real environments, addressing both speaker and vehicle tracking. The comparison with a couple of trackers, relying on the acoustic map only, shows a sharp improvement in performance, paving the way to the application of audio tracking in real challenging environments.

Planar arrays are used jointly with filter-and-sum beamforming to achieve 3-D spatial discrimination in processing broadband signals. In these systems, the beams are steered in various directions to investigate a given portion of space. The band can be so wide as to require both superdirective performance (to increase directivity at low frequencies) and sparse aperiodic layouts (to avoid grating lobes at high frequencies). We propose an original method to simultaneously optimize the transducer positions and the coefficients of the Finite Impulse Response FIR filters, providing a solution that maintains its validity for whatever steering direction inside a predefined region of interest. A hybrid strategy, analytical for the coefficients and stochastic for the positions, is devised to minimize the beam pattern (BP) energy while maintaining an unaltered signal from the steering direction and controlling the side lobes. The robustness of the superdirectivity is achieved by taking into account the probability density functions for the characteristics of realistic transducers. A distinctive feature of our method is its ability to maintain the computational tractability of the addressed optimization problem by drastically reducing the burden of evaluating the cost function. The obtained results, addressing tens of transducers and several octaves of band, demonstrate the effectiveness of the proposed method in terms of directivity, contrast, and robustness.

The estimation of the contrast agent concentration is important for the introduction of effective quantification in ultrasound medical diagnoses. This paper focuses on the comparison of three methods for estimating the number of microbubbles in a small volume. An energy-based linear regression approach and a nonlinear regression approach are applied to estimate the microbubble concentration working from the signals backscattered by a contrast agent population. The nonlinear regression approach, based on the support vector machine, is employed assuming either only one feature, the signal energy, or a set of significant features. Synthetic data are used for the training phase and the test of the methods. The results demonstrate that, although the energy-based methods allows to achieve good results in terms of mean estimated values, the adoption of a vector of features significantly reduces the mean square error. The nonlinear regression approach with multidimensional feature vector allows to improve the measurement precision of the bubble concentration.

This paper addresses the spatial processing of signals collected by a linear array of sensors that feeds a filter-and-sum, data-independent beamformer. When the frequency band spanned by the signals to be processed is extremely wide, a given array can be shorter than the wavelength (at the lowest frequencies) and, at the same time, too scarcely populated for a correct sampling of the wavefield (at the highest frequencies). Superdirectivity and aperiodic sparse layouts are possible solutions to these two problems. However, these two solutions have never been considered jointly to achieve a broadband beam pattern with a desired profile. Through a mixed stochastic and analytic optimization, a method is proposed herein that synthesizes a sparse array layout and the tap coefficients of the beamformer filters to provide a broadband beam pattern that is superdirective and robust against the fluctuation of the sensors' characteristics, which is free from any grating lobes and possesses a controlled side-lobe level. Different types of beam patterns, from the frequency-invariant pattern to the maximum-directivity pattern, can be obtained and the synthesized solutions retain their validity for any steering direction inside a given interval. The functioning of the method is proven by considering a microphone array with four different design targets and by discussing the performance and the robustness of the synthesized solutions.

In this paper, the harmonic beamforming, a technique proposed by the authors to improve the harmonic imaging process in medical echography, is shortly presented. It relies on a modification to the delays used in reception beamforming that allows one to sharply reduce the echoes centered at the fundamental frequency and to keep the echoes centered at the second harmonic, while preserving the focusing performances. The harmonic beamforming is then assessed in term of beam pattern, harmonic-to-fundamental ratio, and range resolution. The tuning of the parameters involved in the harmonic beamforming and the related effects are investigated. The results are compared with those provided by traditional methods of harmonic imaging, clearly showing some advantages in the application of the proposed beamforming scheme.

This paper describes a set of methods that make it possible to estimate the position of a feature inside a three-dimensional (3D) space by starting from a sequence of two-dimensional (2D) acoustic images of the seafloor acquired with a sonar system. Typical sonar imaging systems are able to generate just 2D images, and the acquisition of 3D information involves sharp increases in complexity and costs. The front-scan sonar proposed in this paper is a new equipment devoted to acquiring a 2D image of the seafloor to sail over, and allows one to collect a sequence of images showing a specific feature during the approach of the ship. This fact seems to make it possible to recover the 3D position of a feature by comparing the feature positions along the sequence of images acquired from different (known) ship positions. This opportunity is investigated in the paper, where it is shown that encouraging results have been obtained by a processing chain composed of some blocks devoted to low-level processing, feature extraction and analysis, a Kalman filter for robust feature tracking, and some ad hoc equations for depth estimation and averaging. A statistical error analysis demonstrated the great potential of the proposed system also if some inaccuracies affect the sonar measures and the knowledge of the ship position. This was also confirmed by several tests performed on both simulated and real sequences, obtaining satisfactory results on both the feature tracking and, above all, the estimation of the 3D position.

Underwater exploration is becoming more and more important for many applications involving physical, biological, geological, archaeological, and industrial issues. This paper aims at surveying the up-to-date advances in acoustic acquisition systems and data processing techniques, especially focusing on three-dimensional (3-D) short-range imaging for scene reconstruction and understanding. In fact, the advent of smarter and more efficient imaging systems has allowed the generation of good quality high-resolution images and the related design of proper techniques for underwater scene understanding. The term acoustic vision is introduced to generally describe all data processing (especially image processing) methods devoted to the interpretation of a scene. Since acoustics is also used for medical applications, a short overview of the related systems for biomedical acoustic image for motion is provided. The final goal of the paper is to establish the state of-the art of the techniques and algorithms for acoustic image generation and processing, providing technical details and results for the most promising techniques, and pointing out the potential capabilities of this technology for underwater scene understanding.

This paper describes a flexible technique to enhance the formation of short-range acoustic images so as to improve image quality and facilitate the tasks of subsequent postprocessing methods. The proposed methodology operates as an ideal interface between the signals formed by a focused beamforming technique (i.e., the beam signals) and the related image, whether a two-dimensional (2-D) or three-dimensional (3-D) one. To this end, a reliability measure has been introduced, called confidence, which allows one to perform a rapid examination of the beam signals and is aimed at accurately detecting echoes backscattered from a scene. The confidence-based approach exploits the physics of the process of image formation and generic a priori knowledge of a scene to synthesize model-based signals to be compared with actual backscattered echoes, giving, at the same time, a measure of the reliability of their similarity. The objectives that can be attained by this method can be summarized in a reduction in artifacts due to the lowering of the side-lobe level, a better lateral resolution, a greater accuracy in range determination, a direct estimation of the reliability of the information acquired, thus leading to a higher image quality and hence a better scene understanding. Tests on both simulated and actual data (concerning both 2-D and 3-D images) show the higher efficiency of the proposed confidence-based approach, as compared with more traditional techniques.

This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired data. Beamforming, a method widely applied in acoustic imaging, is used to build a three-dimensional image, associated point by point with another kind of information representing the reliability (i.e. "confidence") of such an image. Unfortunately, this kind of images is plagued by several problems due to the nature of the signal and to the related sensing system, thus heavily affecting data quality. Specifically, speckle noise and the broad directivity characteristic of the sensor lead to very degraded images. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields whose associated probability distributions are specified by a single energy functional. A three-fold process has been applied able to reconstruct, segment, and restore the involved acoustic images exploiting both types of data. Our approach showed better performances with respect to other MRF-based methods as well as classical methods disregarding reliability information. Optimal (in the Maximum A-Posteriori probability sense) estimates of the 3D and confidence images are obtained by minimizing the energy functional by using simulated annealing.

Describes a probabilistic technique for the coupled reconstruction and restoration of underwater acoustic images. The technique is founded on the physics of the image-formation process. Beamforming, a method widely applied in acoustic imaging, is used to build a range image from backscattered echoes, associated point by point with another type of information representing the reliability (or confidence) of such an image. Unfortunately, this kind of images is plagued by problems due to the nature of the signal and to the related sensing system. In the proposed algorithm, the range and confidence images are modeled as Markov random fields whose associated probability distributions are specified by a single energy function. This function has been designed to fully embed the physics of the acoustic image-formation process by modeling a priori knowledge of the acoustic system, the considered scene, and the noise-affecting measures and also by integrating reliability information to allow the coupled and simultaneous reconstruction and restoration of both images. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed range image map and the restored confidence image are obtained by minimizing the energy function using simulated annealing. Experimental results show the improvement of the processed images over those obtained by other methods performing separate reconstruction and restoration processes that disregard reliability information.

We address the problem of the recognition of man-made objects and the estimation of the related orientation in 2D acoustic images acquired with a forward looking sonar or an acoustic camera. A voting-based approach is described that is able to recognize objects and to estimate their two-dimensional pose by using information coming from boundary segments and their angular relations. The method is directly applied to the edge discontinuities of underwater acoustic images, whose quality is usually affected by some undesired effects such as object blurring, speckle noise, and geometrical distortions degrading the edge detection. The voting approach is robust with respect to these effects, so that good results are obtained even with images of poor quality. The sequences of simulated and real acoustic images are presented in order to test the validity of the proposed method in terms of the average estimation error and computational load.

A method to improve the quality of acoustic images obtained by systems based on beamforming is described. The specific goal is to eliminate two additive spurious terms present in the beam signals. The first effect is due to side lobes and the second one is due to the nonlinear effects introduced by the modulus extractor present at the output of the beamformer. The relative importance of these two terms is assessed, showing that the term due to modulus extraction has a strong negative incidence on the final image quality. The Gaussian distribution of these spurious terms is discussed and verified in the case of punctiform scenes. Owing to the Gaussianity of the spurious terms, a method to eliminate them from the beam signals, based on higher order spectra analysis, is proposed, resulting in an evident image quality and reliability improvement.

Describes an application of the dynamic focusing technique for underwater 3D acoustic imaging. In particular, the integration of dynamic focalization with an FFT implementation of phase-shift beamforming has been developed in order to avoid an increase in computational complexity. The resulting system, which uses narrow-band signals, permits the formation of 3D images at a high frame rate and without the limitations imposed by the depth of field on fixed-focused beamforming. Some simulations made it possible to test the proposed system, and the related results are reported.>