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The U.S. Army Engineer Research and Development Center (ERDC) has developed an underwater wireless modem using low frequency (LF) electromagnetic communication with application to Structural Health Monitoring (SHM) and nondestructive evaluation (NDE) of partially submerged large civil infrastructure, such as navigational locks. Environmental constraints with commonly used underwater wireless communication techniques have motivated the investigation of electromagnetic telemetry. The ongoing research has resulted in an integrated hardware solution with current focus on software development, sensor integration, and deployment testing. Ultimately, the underwater telemetry modems are expected to provide a method for The U.S. Army Corps of Engineers to improve data collection and process control for monitoring of structures in our navigable inland waterways. This paper highlights the recent and ongoing progress of the project and anticipates possible applications of the technology to other extreme environments.

An embedded strain gage data logger has been developed by the Engineer Research and Development Center (ERDC) to facilitate data collection for an in-situ experiment investigating the strain and acceleration encountered by the paddlefish rostrum as it swims. This unique application has required a focus on reducing the size, weight, and power (SWaP) of the system. The accomplishments explained in this paper are the integration of a custom strain gage amplifier with a microcontroller and the verification of a linear relationship of the strain measured by this device with a calibrated professional desktop system. The mean and standard deviation errors between the expected values of the calibrated commercial system and the data logger from this experiment are −7/.7 × 10−14 μϵ and 14.3 μϵ respectively.

Domestic military installations generate high levels of noise due to testing and training which leads to annoyance and complaints from surrounding communities. This necessitates continuous noise monitoring to provide decision makers with the information they need to proactively manage their noise environment. Due to the diverse climates in which military testing and training are conducted (e.g., desert, tundra, and rainforest), monitoring equipment that can operate in a variety of environmental conditions with minimal maintenance and low power consumption is needed. Using existing technologies as a baseline, various iterations of a low-cost acoustic monitor were designed to meet these constraints while minimizing initial investment cost, improving the mean time between failures, and increasing overall system capability. This paper will describe the system developed to provide a rapid deployment option that is robust to extreme temperatures, humidity, and destructive wildlife. A review of operational logs collected during multiple deployments was used to evaluate system performance against benchtop and off-the-shelf solutions. This data demonstrate the reliability of the monitoring stations and the sustainability of their hardware.Domestic military installations generate high levels of noise due to testing and training which leads to annoyance and complaints from surrounding communities. This necessitates continuous noise monitoring to provide decision makers with the information they need to proactively manage their noise environment. Due to the diverse climates in which military testing and training are conducted (e.g., desert, tundra, and rainforest), monitoring equipment that can operate in a variety of environmental conditions with minimal maintenance and low power consumption is needed. Using existing technologies as a baseline, various iterations of a low-cost acoustic monitor were designed to meet these constraints while minimizing initial investment cost, improving the mean time between failures, and increasing overall system capability. This paper will describe the system developed to provide a rapid deployment option that is robust to extreme temperatures, humidity, and destructive wildlife. A review of operational logs ...

Current methods for direction of arrival (DOA)estimation are disproportionately represented in the literature by microphone array geometry and sound source properties. A wide variety of implemented methods and publications are available for uniformly-spaced arrays such as uniform linear arrays (ULA), uniform circular arrays (UCA), and uniform rectangular arrays (URA). Further, implemented DOA estimators are specifically designed for narrowband, continuous signals. Methods applicable to wideband signals on arbitrarily-shaped arrays are limited; alternative approaches that partition the array into sub arrays expand the number of applicable methods. For a realistic military application of a single impulse localization on a conformal microphone array, methods must be able to estimate the DOA of wideband, static, acoustic sources. DOA estimator methods’ performances, capabilities, and limitations are explored on various real-world sound sources and configurations of a five-microphone conformal array.

Modern automated acoustic classifiers have been shown to perform remarkably well with human speech recognition and music genre classification. These problems are well defined; there is a deep understanding of the source signal, and the required robustness of the model can be decreased without significantly sacrificing accuracy. Unfortunately, this simplification creates models that are insufficient when tasked with classifying environmental noise, which is inherently more variable and difficult to constrain. To further close the gap between human and computer recognition, we must find feature extraction techniques that address the additional set of complexities involved with environmental noise. In this paper, we will explore sophisticated feature extraction techniques (e.g., convolutional auto-encoders and scattering networks), and discuss their effect when applied to acoustic classification.

With the advent of reliable and continuously operating noise monitoring systems, we are now faced with an unprecedented amount of noise monitor data. In the context of environmental noise monitoring, there is a need to automatically detect, separate, and classify all environmental noise sources. This is a complex task because sources can overlap, vary by location, and have an unbounded number of noise sources that a monitor device may record. In this study, we synthetically generate datasets that contain Gaussian noise and overlaps for several pre-labeled environmental noise monitoring datasets to examine how well deep learning methods (e.g., autoencoders) can separate environmental noise sources. In addition to examining performance, we also focus on understanding which signal features and separation metrics are useful to this problem.

Deep learning is becoming ubiquitous; it is the underlying and driving force behind many heavily embedded technologies in society (e.g., search engines, fraud detection warning systems, and social-media facial recognition algorithms). Over the past few years there has been a steady increase in the number of audio related applications of deep learning. Recently, Nykaza et al. presented a pedagogical approach to understanding how the hidden layers recreate, separate, and classify environmental noise signals. That work presented some feature extraction examples using simple pure tone, chord, and environmental noise datasets. In this paper, we build upon this recent analysis and expand the datasets to include more realistic representations of those datasets with the inclusion of noise and overlapping signals. Additionally, we consider other related architectures (e.g., variant-autoencoders, recurrent neural networks, and fixing hidden nodes/layers), explore their advantages/drawbacks, and provide insights on ...

Environmental noise can cause sleep disturbance, annoyance, complaints, and quite possibly adverse health effects. This is true for continuous noise sources such as steady road traffic noise, impulsive noise sources such as blasts or sonic booms, or sources that fall in-between such as intermittent train and aircraft noise. One way to manage environmental noise is to use noise-monitoring technology to provide both the noise-producers and noise-experiencers feedback on the actual noise environment. Traditional noise-monitoring systems, however, only provide this information at a few locations resulting in an incomplete picture of the noise environment over the entire regions of interest. In this paper, we discuss a framework for providing real-time feedback of the noise environment over a large area (e.g., 100 km2). We show all the steps that are needed to convert the raw noise-monitor data into noise maps and noise impact maps to help manage environmental noise. We discuss the complexity of the problem an...

Deep learning is becoming ubiquitous; it is the underlying and driving force behind many technologies we use everyday (e.g., search engines, fraud detection warning systems, and social-media facial recognition algorithms). Over the past few years, there has been a steady increase in the number of audio and acoustics related applications of deep learning. But what is exactly going on under the hood? In this paper, we focus on deep learning algorithms for unsupervised feature learning. We take a pedagogical approach to understanding how the hidden layers recreate, separate, and classify audio signals. We begin with a simple pure tone dataset, and systematically increase the complexity of this dataset in both frequency and time. We end the presentation with some feature extraction examples from real-world environmental recordings, and find that these features are easier to interpret given the understanding developed from the simpler tone datasets. The unsupervised feature learning techniques explored in this paper include: restricted Boltzmann machines (RBMs) and auto-encoders (AEs).