BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data.

Sound source localization using multichannel signal processing has been a subject of active research for decades. In recent years, the use of deep learning in audio signal processing has significantly improved the performances for machine hearing. This has motivated the scientific community to also develop machine learning strategies for source localization applications. This paper presents BeamLearning, a multiresolution deep learning approach that allows the encoding of relevant information contained in unprocessed time-domain acoustic signals captured by microphone arrays. The use of raw data aims at avoiding the simplifying hypothesis that most traditional model-based localization methods rely on. Benefits of its use are shown for real-time sound source two-dimensional localization tasks in reverberating and noisy environments. Since supervised machine learning approaches require large-sized, physically realistic, precisely labelled datasets, a fast graphics processing unit-based computation of room impulse responses was developed using fractional delays for image source models. A thorough analysis of the network representation and extensive performance tests are carried out using the BeamLearning network with synthetic and experimental datasets. Obtained results demonstrate that the BeamLearning approach significantly outperforms the wideband MUSIC and steered response power-phase transform methods in terms of localization accuracy and computational efficiency in the presence of heavy measurement noise and reverberation.

Hemispherical double-layer time reversal imaging in reverberant and noisy environments at audible frequencies.

Time reversal is a widely used technique in wave physics, for both imaging purposes and experimental focusing. In this paper, a complete double-layer time reversal imaging process is proposed for in situ acoustic characterization of non-stationary sources, with perturbative noise sources and reverberation. The proposed method involves the use of a hemispherical array composed of pressure-pressure probes. The complete set of underlying optimizations to sonic time reversal imaging is detailed, with regard to space and time reconstruction accuracy, imaging resolution and sensitivity to reverberation, and perturbative noise. The proposed technique is tested and compared to more conventional time reversal techniques through numerical simulations and experiments. Results demonstrate the ability of the proposed method to back-propagate acoustic waves radiated from non-stationary sources in the volume delimited by the measurement array with a high precision both in time and space domains. Analysis of the results also shows that the process can successfully be applied in strongly reverberant environments, even with poor signal-to-noise ratio.

Evaluation of a separation method for source identification in small spaces.

This paper investigates the efficiency of a field separation method for the identification of sound sources in small and non-anechoic spaces. When performing measurements in such environments, the acquired data contain information from the direct field radiated by the source of interest and reflections from walls. To get rid of the unwanted contributions and assess the field radiated by the source of interest, a field separation method is used. Acoustic data (pressure or velocity) are then measured on a hemispheric array whose base is laying on the surface of interest. Then, by using spherical harmonic expansions, contributions from outgoing and incoming waves can be separated if the impedance of the tested surface is high enough. Depending on the probe type, different implementations of the separation method are numerically compared. In addition, the influence of the walls' reflection coefficient is studied. Finally, measurements are performed using an array made-up of 36 p-p probes. Results obtained in a car trunk mock-up with controlled sources are first presented before reporting results measured in a real car running on a roller bench.

Quantitative elastography of renal transplants using supersonic shear imaging: a pilot study.

PURPOSE: To evaluate the reliability of quantitative ultrasonic measurement of renal allograft elasticity using supersonic shear imaging (SSI) and its relationship with parenchymal pathological changes. MATERIALS AND METHODS: Forty-three kidney transplant recipients (22 women, 21 men) (mean age, 51 years; age range, 18-70 years) underwent SSI elastography, followed by biopsy. The quantitative measurements of cortical elasticity were performed by two radiologists and expressed in terms of Young's modulus (kPa). Intra- and inter-observer reproducibility was assessed (Kruskal-Wallis test and Bland-Altman analysis), as well as the correlation between elasticity values and clinical, biological and pathological data (semi-quantitative Banff scoring). Interstitial fibrosis was evaluated semi-quantitatively by the Banff score and measured by quantitative image analysis. RESULTS: Intra- and inter-observer variation coefficients of cortical elasticity were 20 % and 12 %, respectively. Renal cortical stiffness did not correlate with any clinical parameters, any single semi-quantitative Banff score or the level of interstitial fibrosis; however, a significant correlation was observed between cortical stiffness and the total Banff scores of chronic lesions and of all elementary lesions (R = 0.34, P = 0.05 and R = 0.41, P = 0.03,respectively). CONCLUSION: Quantitative measurement of renal cortical stiffness using SSI is a promising non-invasive tool to evaluate global histological deterioration. KEY POINTS : • Supersonic shear imaging elastography can measure cortical stiffness in renal transplants • The level of cortical stiffness is correlated with the global degree of tissue lesions • The global histological deterioration of transplanted kidneys can be quantified using elastography.

Noninvasive in vivo liver fibrosis evaluation using supersonic shear imaging: a clinical study on 113 hepatitis C virus patients.

Supersonic shear imaging (SSI) has recently been demonstrated to be a repeatable and reproducible transient bidimensional elastography technique. We report a prospective clinical evaluation of the performances of SSI for liver fibrosis evaluation in 113 patients with hepatitis C virus (HCV) and a comparison with FibroScan (FS). Liver elasticity values using SSI and FS ranged from 4.50 kPa to 33.96 kPa and from 2.60 kPa to 46.50 kPa, respectively. Analysis of variance (ANOVA) shows a good agreement between fibrosis staging and elasticity assessment using SSI and FS (p < 10(-5)). The areas under receiver operating characteristic (ROC) curves for elasticity values assessed from SSI were 0.948, 0.962 and 0.968 for patients with predicted fibrosis levels F ≥ 2, F ≥ 3 and F = 4, respectively. These values are compared with FS area under the receiver operating characteristic curve (AUROC) of 0.846, 0.857 and 0.940, respectively. This comparison between ROC curves is particularly significant for mild and intermediate fibrosis levels. SSI appears to be a fast, simple and reliable method for noninvasive liver fibrosis evaluation.