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Learning-based estimation of in-situ wind speed from underwater acoustics
[article]
2022
arXiv
pre-print
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Here, we introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics possibly complemented by other data sources such as weather model reanalyses. ...
Whereas model-driven schemes, especially data assimilation approaches, are the state-of-the-art schemes to address inverse problems in geoscience, machine learning techniques become more and more appealing ...
Acknowledgment This work is funded by the AI Chair Oceanix (ANR grant ANR-19-CHIA-0016) and is supported by the industrial partnership with Naval Group. ...
arXiv:2208.08912v1
fatcat:tdnopcvoanechitp2g766jvjoe
Accurate Channel Estimation and Adaptive Underwater Acoustic Communications Based on Gaussian Likelihood and Constellation Aggregation
2022
Sensors
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We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization ...
Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. ...
Therefore, the second scheme is more suitable for fast time-varying channels incurred by underwater acoustic communications with moving transceivers than the first scheme. ...
doi:10.3390/s22062142
pmid:35336313
pmcid:PMC8955422
fatcat:tiym3fwoljc5flnlacufslo6ve
Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks
2020
Scientific Reports
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AbstractThis paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration ...
Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. ...
And it is supported by Department of Energy grants DE-FE00029063 and DE-AC07-05ID14517. ...
doi:10.1038/s41598-020-77147-2
pmid:33273503
fatcat:r2cedrmekbd27dels2snlexofm
Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range
[article]
2022
arXiv
pre-print
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Significant wave height, dominant wave frequency were estimated as the result of such signals processing with the use of machine learning tools. ...
Wind waves prediction is in a good agreement with direct measurements, made on the platform, and machine learning results allow physical interpretation. ...
RMSE = 1 n n ∑ i=1 (Y i − Ŷi ) 2 (9)
Results
Correlation analysis Prior to exploiting machine learning methods we start from direct comparison of environmental parameters and acoustic signal features ...
arXiv:2204.10153v2
fatcat:faossdy5gvf3xntdhhlak3xuju
Blind Reverberation Time Estimation in Dynamic Acoustic Conditions
[article]
2022
arXiv
pre-print
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Motivated by a recent trend towards data-centric approaches in machine learning, we propose a novel way of generating training data and demonstrate, using an existing deep neural network architecture, ...
The estimation of reverberation time from real-world signals plays a central role in a wide range of applications. ...
Motivated by a recent shift from a model-centric towards a data-centric approach to various problems in machine learning, we propose a novel way of generating training data and investigate its effect on ...
arXiv:2202.11790v1
fatcat:duvdfnrskzcptpqyhcnljt5b6y
Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: a Test Case for Pelagic Fish
2021
Zenodo
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Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. ...
To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. ...
Different than current fish detection sonar systems that can detect low power acoustic reflections by applying directionality from an array of receivers, we aim for omni-directional detection by a single ...
doi:10.5281/zenodo.4983077
fatcat:bcfqvw5v6nc2bp2ddimfb423zu
Leveraging the Channel as a Sensor: Real-time Vehicle Classification Using Multidimensional Radio-fingerprinting
[article]
2018
arXiv
pre-print
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of more than 99% and an overall accuracy of 89.15% for a fine-grained classification task with nine different classes. ...
In this paper, we present a system for classifying vehicles based on their radio-fingerprints which applies cutting-edge machine learning models and can be non-intrusively installed into the existing road ...
ACKNOWLEDGMENT Part of the work on this paper has been supported by the German Federal Ministry for Economic Affairs and Energy as part of the cooperation project between Wilhelm Schröder GmbH, TU Dortmund ...
arXiv:1807.00464v1
fatcat:zrtdad3qczczjizskl24ottloy
Marine Wireless Big Data: Efficient Transmission, Related Applications, and Challenges
2018
IEEE wireless communications
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2017 pre-print arXiv:1710.04012v1 fatcat:ya5l7a4vhbgxbbnbovak5oner4
We then investigate the possibilities of and develop the schemes for energy-efficient and reliable undersea transmission without or slightly with data rate reduction. ...
In this article, we first propose an architecture of heterogeneous marine networks that flexibly exploits the existing underwater wireless techniques as a potential solution for fast data transmission. ...
Fig. 3 . 3 Adaptive decision feedback equalization combined with channel estimation.
Fig. 5 . 5 An example of a data-driven deep learning algorithm for marine object recognition. ...
doi:10.1109/mwc.2018.1700192
fatcat:c5tq6y4qizf3fp6u5o4ika2kgu
Detecting Submerged Objects Using Active Acoustics and Deep Neural Networks: a Test Case for Pelagic Fish
2020
IEEE Transactions on Mobile Computing
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Here we present a deep learning approach to detect the pattern of a moving fish from the reflections of an active acoustic emitter. ...
To allow for real-time detection, we use a convolutional neural network, which provides the simultaneous labeling of a large buffer of signal samples. ...
Different than current fish detection sonar systems that can detect low power acoustic reflections by applying directionality from an array of receivers, we aim for omni-directional detection by a single ...
doi:10.1109/tmc.2020.3044397
fatcat:djjlzpgsfnfflcevcil347pnbu
A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
[article]
2020
arXiv
pre-print
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The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial directions or locations while ...
The two key differences are a more diverse range of acoustical conditions, and dynamic conditions, i.e. moving sources. ...
or acoustic direction-of-arrival (DoA). ...
arXiv:2006.01919v2
fatcat:ap7bknscobb23c56lp3nvdf54q
A Review of Underwater Mine Detection and Classification in Sonar Imagery
2021
Electronics
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The author considered current and previous generation methods starting with classical image processing, and then machine learning followed by deep learning. ...
One of the measures used by MCM units is mine hunting, which requires searching for all the mines in a suspicious area. ...
As for semi-supervised learning, it combines a small amount of labelled data with a large amount of unlabelled data. ...
doi:10.3390/electronics10232943
fatcat:gwfcc4ypcnc25im7fbejjz5au4
Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches
2021
Sensors
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In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination ...
Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. ...
We mainly dealt with the overview of various touchscreen schemes from resistive to optical methods, and two main research directions of SNR improvement and stylus support as well as machine learning approaches ...
doi:10.3390/s21144776
fatcat:3nlokbjpmvguzcdumahe364nay
On the similarities of representations in artificial and brain neural networks for speech recognition
2022
Frontiers in Computational Neuroscience
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same speech.ResultsIn one direction, we found a quasi-hierarchical functional organization in human auditory cortex qualitatively matched with the hidden layers of deep artificial neural networks trained ...
IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. ...
Acknowledgments The authors thank Anastasia Klimovich-Smith, Hun Choi, Lorraine Tyler, Andreas Marouchos, and Geoffrey Hinton for thoughtful comments and discussions. ...
doi:10.3389/fncom.2022.1057439
pmid:36618270
pmcid:PMC9811675
fatcat:sxwbzkxcrfan3lrnnej7kkmhbm
Mapping between acoustic and articulatory gestures
2011
Speech Communication
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The Acoustic-to-Articulatory Inversion is performed using a GMM-based regression and the results are at par with state-of-the-art frame-based methods with dynamical constraints (with an average error of ...
A definition for these gestures along with a method to segment the measured articulatory trajectories and the acoustic waveform into gestures is suggested. ...
of acoustics and articulatory movements, thereby reducing the load on the machine learning algorithm. ...
doi:10.1016/j.specom.2011.01.009
fatcat:ulav2err4jewtfl27gjp6mect4
The Channel as a Traffic Sensor: Vehicle Detection and Classification based on Radio Fingerprinting
[article]
2020
arXiv
pre-print
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a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes. ...
In this paper, we present a novel approach, which exploits radio fingerprints - multidimensional attenuation patterns of wireless signals - for accurate and robust vehicle detection and classification. ...
Fig. 11 . 11 Comparison of the overall classification accuracies for the considered machine learning models and vehicle classification schemes. ...
arXiv:2003.09827v1
fatcat:q3rn4z3vjrfp5cv3uv7pkocoji
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