Design and implementation of a multi-octave-band audio camera for realtime diagnosis

Noise pollution investigation takes advantage of two common methods of diagnosis: measurement using a Sound Level Meter and acoustical imaging. The former enables a detailed analysis of the surrounding noise spectrum whereas the latter is rather used for source localization. Both approaches complete each other, and merging them into a unique system, working in realtime, would offer new possibilities of dynamic diagnosis. This paper describes the design of a complete system for this purpose: imaging in realtime the acoustic field at different octave bands, with a convenient device. The acoustic field is sampled in time and space using an array of MEMS microphones. This recent technology enables a compact and fully digital design of the system. However, performing realtime imaging with resource-intensive algorithm on a large amount of measured data confronts with a technical challenge. This is overcome by executing the whole process on a Graphic Processing Unit, which has recently become an attractive device for parallel computing

An experimental benchmark for geoacoustic inversion methods

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Pecknold, S. P., Hines, P. C., & Chapman, N. R. An experimental benchmark for geoacoustic inversion methods. IEEE Journal of Oceanic Engineering, 46(1), (2021): 261-282, https://doi.org/10.1109/JOE.2019.2960879.Over the past 25 years, there has been significant research activity in development and application of methods for inverting acoustical field data to estimate parameters of geoacoustic models of the ocean bottom. Although the performance of various geoacoustic inversion methods has been benchmarked on simulated data, their performance with experimental data remains an open question. This article constitutes the first attempt of an experimental benchmark of geoacoustic inversion methods. To do so, the article focuses on data from experiments carried out at a common site during the Shallow Water 2006 (SW06) experiment. The contribution of the article is twofold. First, the article provides an overview of experimental inversion methods and results obtained with SW06 data. Second, the article proposes and uses quantitative metrics to assess the experimental performance of inversion methods. From a sonar performance point of view, the benchmark shows that no particular geoacoustic inversion method is definitely better than any other of the ones that were tested. All the inversion methods generated adequate sound-speed profiles, but only a few methods estimated attenuation and density. Also, acoustical field prediction performance drastically reduces with range for all geoacoustic models, and this performance loss dominates over intermodel variability. Overall, the benchmark covers the two main objectives of geoacoustic inversion: obtaining geophysical information about the seabed, and/or predicting acoustic propagation in a given area.Funding Agency: U.S. Office of Naval Research; Ocean Acoustics

Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring

A Geometric Approach to Covariance Matrix Estimation and its Applications to Radar Problems

A new class of disturbance covariance matrix estimators for radar signal processing applications is introduced following a geometric paradigm. Each estimator is associated with a given unitary invariant norm and performs the sample covariance matrix projection into a specific set of structured covariance matrices. Regardless of the considered norm, an efficient solution technique to handle the resulting constrained optimization problem is developed. Specifically, it is shown that the new family of distribution-free estimators shares a shrinkagetype form; besides, the eigenvalues estimate just requires the solution of a one-dimensional convex problem whose objective function depends on the considered unitary norm. For the two most common norm instances, i.e., Frobenius and spectral, very efficient algorithms are developed to solve the aforementioned one-dimensional optimization leading to almost closed form covariance estimates. At the analysis stage, the performance of the new estimators is assessed in terms of achievable Signal to Interference plus Noise Ratio (SINR) both for a spatial and a Doppler processing assuming different data statistical characterizations. The results show that interesting SINR improvements with respect to some counterparts available in the open literature can be achieved especially in training starved regimes.Comment: submitted for journal publicatio

Seabed classification using physics-based modeling and machine learning

In this work model-based methods are employed along with machine learning techniques to classify sediments in oceanic environments based on the geoacoustic properties of a two-layer seabed. Two different scenarios are investigated. First, a simple low-frequency case is set up, where the acoustic field is modeled with normal modes. Four different hypotheses are made for seafloor sediment possibilities and these are explored using both various machine learning techniques and a simple matched-field approach. For most noise levels, the latter has an inferior performance to the machine learning methods. Second, the high-frequency model of the scattering from a rough, two-layer seafloor is considered. Again, four different sediment possibilities are classified with machine learning. For higher accuracy, 1D Convolutional Neural Networks (CNNs) are employed. In both cases we see that the machine learning methods, both in simple and more complex formulations, lead to effective sediment characterization. Our results assess the robustness to noise and model misspecification of different classifiers

Compact CRB for delay, Doppler, and phase estimation – application to GNSS SPP and RTK performance characterisation

The derivation of tight estimation lower bounds is a key tool to design and assess the performance of new estimators. In this contribution, first, the authors derive a new compact Cramér–Rao bound (CRB) for the conditional signal model, where the deterministic parameter's vector includes a real positive amplitude and the signal phase. Then, the resulting CRB is particularised to the delay, Doppler, phase, and amplitude estimation for band-limited narrowband signals, which are found in a plethora of applications, making such CRB a key tool of broad interest. This new CRB expression is particularly easy to evaluate because it only depends on the signal samples, then being straightforward to evaluate independently of the particular baseband signal considered. They exploit this CRB to properly characterise the achievable performance of satellite-based navigation systems and the so-called real-time kinematics (RTK) solution. To the best of the authors’ knowledge, this is the first time these techniques are theoretically characterised from the baseband delay/phase estimation processing to position computation, in terms of the CRB and maximum-likelihood estimation

Amplitude and phase sonar calibration and the use of target phase for enhanced acoustic target characterisation

This thesis investigates the incorporation of target phase into sonar signal processing, for enhanced information in the context of acoustical oceanography. A sonar system phase calibration method, which includes both the amplitude and phase response is proposed. The technique is an extension of the widespread standard-target sonar calibration method, based on the use of metallic spheres as standard targets. Frequency domain data processing is used, with target phase measured as a phase angle difference between two frequency components. This approach minimizes the impact of range uncertainties in the calibration process. Calibration accuracy is examined by comparison to theoretical full-wave modal solutions. The system complex response is obtained for an operating frequency of 50 to 150 kHz, and sources of ambiguity are examined. The calibrated broadband sonar system is then used to study the complex scattering of objects important for the modelling of marine organism echoes, such as elastic spheres, fluid-filled shells, cylinders and prolate spheroids. Underlying echo formation mechanisms and their interaction are explored. Phase-sensitive sonar systems could be important for the acquisition of increased levels of information, crucial for the development of automated species identification. Studies of sonar system phase calibration and complex scattering from fundamental shapes are necessary in order to incorporate this type of fully-coherent processing into scientific acoustic instruments