Deep learning for unsupervised separation of environmental noise sources

Wilkinson, Bryan; Ellison, Charlotte L.; Nykaza, Edward T.; Boedihardjo, Arnold P.; Netchaev, Anton; Bunkley, Steven L.; Oates, Tim; Blevins, Matthew G.

doi:10.1121/1.4989024

Cited by 3 publications

(1 citation statement)

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“…Lane et al [128] created a mobile application capable of performing very accurate speaker diarization and emotion recognition using deep learning. Recently, Wilkinson et al [129] performed unsupervised separation of environmental noise sources adding artificial Gaussian noise to pre-labeled signals and used auto-encoders to cluster. However, background noise in an environmental signal is usually non-Gaussian, making this method to work on specific datasets only.…”

Section: Acoustic Sensormentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

148

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Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.

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Section: Acoustic Sensormentioning

confidence: 99%

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Samaras

Diamantidou

Ataloglou

et al. 2019

Sensors

148

View full text Add to dashboard Cite

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Dynamic Schwarz Meta‐Foams: Customizable Solutions for Environmental Noise Reduction

Saatchi,

Oh,

Yoo

et al. 2024

Advanced Science

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In an era marked by increasing environmental challenges affecting human well‐being, traditional acoustic materials struggle to effectively handle the diverse and multi‐frequency nature of harmful environmental noises. This has spurred a demand for innovative acoustic metamaterial solutions by utilizing sustainable design strategies. This research introduces tunable Schwarz metamaterial capable of transforming into a soft meta‐foam to solve the complex problems of varying environmental noises. This study primarily focuses on adjusting single to multiple sound‐blocking bandgaps mechanism using a multi‐layered approach, incorporating the Schwarz P‐type triply periodic minimal surface (TPMS) and its elective soft foam counterpart, known as tunable Schwarz meta‐foams (TSMF‐x). The tunable design parameters of the unit cell, multi‐layered TPMS, and soft programmable TSMF‐lichen version are comprehensively explored including a fire‐safety test. The results demonstrate these enhanced flame retardant meta‐foam families have the potential to be used for mid‐to‐high‐frequency environmental noises in industrial equipment and smart homes for sustainable architecture and environmental health applications.

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Detection and recognition of UA targets with multiple sensors

Chen

Chen²,

Liu

et al. 2022

Aeronaut. j.

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Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

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Deep learning for unsupervised separation of environmental noise sources

Cited by 3 publications

References 0 publications

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review

Dynamic Schwarz Meta‐Foams: Customizable Solutions for Environmental Noise Reduction

Detection and recognition of UA targets with multiple sensors

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