Blind channel estimation of time-varying underwater acoustic waveguide impulse responses

Rideout, B.; Nosal, Eva‐Marie; Høst-Madsen, A.

doi:10.1121/1.4970720

Cited by 2 publications

(3 citation statements)

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“…These methods belong to a larger class of methods targeted at impulse-like sounds such as sperm whale clicks or to narrowband sounds produced in large non-reverberant environments (where reflections are sparse), in which direct-path and secondary-path waveforms are separable and amenable to geometric interpretation [27,[52][53][54]. Perhaps more pertinent to narrowband sounds in reverberant environments is a method recently proposed for transforming complex waveforms into impulse-like waveforms (impulse response functions) for easier TDOA extraction [55], however at present this method has not been tested for marine mammal call localization in any context.…”

Section: Background and Significancementioning

confidence: 99%

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool

2020

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Tracking the origin of propagating wave signals in an environment with complex reflective surfaces is, in its full generality, a nearly intractable problem which has engendered multiple domain-specific literatures. We posit that, if the environment and sensor geometries are fixed, machine learning algorithms can "learn" the acoustical geometry of the environment and accurately track signal origin. In this paper, we propose the first machine-learningbased approach to identifying the source locations of semi-stationary, tonal, dolphin-whistle-like sounds in a highly reverberant space, specifically a half-cylindrical dolphin pool. Our algorithm works by supplying a learning network with an overabundance of location "clues", which are then selected under supervised training for their ability to discriminate source location in this particular environment. More specifically, we deliver estimated time-difference-of-arrivals (TDOA's) and normalized cross-correlation values computed from pairs of hydrophone signals to a random forest model for high-feature-volume classification and feature selection, and subsequently deliver the selected features into linear discriminant analysis, linear and quadratic Support Vector Machine (SVM), and Gaussian process models. Based on data from 14 sound source locations and 16 hydrophones, our classification models yielded perfect accuracy at predicting novel sound source locations. Our regression models yielded better accuracy than the established Steered-Response Power (SRP) method when all training data were used, and comparable accuracy along the pool surface when deprived of training data at testing sites; our methods additionally boast improved computation time and the potential for superior localization accuracy in all dimensions with more training data. Because of the generality of our method we argue it may be useful in a much wider variety of contexts.

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Section: Background and Significancementioning

confidence: 99%

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool

2020

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“…It is straightforward to include the "soft" TV term in the gradients, and we can use standard first-order algorithms to solve the augmented problem. Simulated data containing an impulsive source, two underwater acoustic channel impulse responses, and the two outputs of the underwater channels were provided by [11]. The simulation also added zero mean Gaussian noise with standard deviation 0.005.…”

Section: Example 2: Blind Channel Estimationmentioning

confidence: 99%

“…We show the output and channel impulse response for only the first channel; the second channel is similar. Researchers studying the acoustic channel are mainly interested in the time delays between peaks and relative phase shifts [11]. Besides the overall magnitude ambiguity and time shift, it appears we can accurately estimate the time delays and phase shifts between the peaks of the channel response.…”

Section: Example 2: Blind Channel Estimationmentioning

confidence: 99%

Efficient Adjoint Computation for Wavelet and Convolution Operators [Lecture Notes]

Folberth

Becker

2016

IEEE Signal Process. Mag.

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First-order optimization algorithms, often preferred for large problems, require the gradient of the differentiable terms in the objective function. These gradients often involve linear operators and their adjoints, which must be applied rapidly. We consider two example problems and derive methods for quickly evaluating the required adjoint operator. The first example is an image deblurring problem, where we must compute efficiently the adjoint of multi-stage wavelet reconstruction. Our formulation of the adjoint works for a variety of boundary conditions, which allows the formulation to generalize to a larger class of problems. The second example is a blind channel estimation problem taken from the optimization literature where we must compute the adjoint of the convolution of two signals. In each example, we show how the adjoint operator can be applied efficiently while leveraging existing software.

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Blind channel estimation of time-varying underwater acoustic waveguide impulse responses

Cited by 2 publications

References 0 publications

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool

Learning to localize sounds in a highly reverberant environment: Machine-learning tracking of dolphin whistle-like sounds in a pool

Efficient Adjoint Computation for Wavelet and Convolution Operators [Lecture Notes]

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