The Experts below are selected from a list of 2976 Experts worldwide ranked by ideXlab platform
Hae-yong Yang - One of the best experts on this subject based on the ideXlab platform.
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Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
arXiv: Sound, 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
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EUSIPCO - Empirical study of drone Sound Detection in real-life environment with deep neural networks
2017 25th European Signal Processing Conference (EUSIPCO), 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
Sungho Jeon - One of the best experts on this subject based on the ideXlab platform.
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Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
arXiv: Sound, 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
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EUSIPCO - Empirical study of drone Sound Detection in real-life environment with deep neural networks
2017 25th European Signal Processing Conference (EUSIPCO), 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
John C Gallagher - One of the best experts on this subject based on the ideXlab platform.
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real time uav Sound Detection and analysis system
Static Analysis Symposium, 2017Co-Authors: Juhyun Kim, Cheonbok Park, Jinwoo Ahn, Junghyun Park, John C GallagherAbstract:In this paper, we present a real-time drone Detection and monitoring system, that users can easily utilize in daily life to detect drones using Sound data. This system performs FFT on the sampled real-time data and performs drone Detection using the transformed data through two different methods, Plotted Image Machine Learning (PIL) and K Nearest Neighbors (KNN). The PIL uses image data from the visualized FFT graph to detect robust points, and compares the average image similarity with a reference FFT template associated with a target of interest. Whereas, the KNN uses FFT-format csv files to compare the average distance similarity. Experiments were performed with the two methods. As a result, the accuracy rate of 83% and 61% was shown in each of PIL and KNN. The major deliverables of this work are a software package framework one may use to experiment with various Sound samples and classifiers via modifiable classifier modules and initial testing of two classifiers. Future work, enabled by the software framework developed, can employ more capable classifiers.
Younghyoun Kwon - One of the best experts on this subject based on the ideXlab platform.
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Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
arXiv: Sound, 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
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EUSIPCO - Empirical study of drone Sound Detection in real-life environment with deep neural networks
2017 25th European Signal Processing Conference (EUSIPCO), 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
Woong-hee Kim - One of the best experts on this subject based on the ideXlab platform.
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Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
arXiv: Sound, 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.
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EUSIPCO - Empirical study of drone Sound Detection in real-life environment with deep neural networks
2017 25th European Signal Processing Conference (EUSIPCO), 2017Co-Authors: Sungho Jeon, Jong-woo Shin, Young-jun Lee, Woong-hee Kim, Younghyoun Kwon, Hae-yong YangAbstract:This work aims to investigate the use of deep neural network to detect commercial hobby drones in real-life environments by analyzing their Sound data. The purpose of work is to contribute to a system for detecting drones used for malicious purposes, such as for terrorism. Specifically, we present a method capable of detecting the presence of commercial hobby drones as a binary classification problem based on Sound event Detection. We recorded the Sound produced by a few popular commercial hobby drones, and then augmented this data with diverse environmental Sound data to remedy the scarcity of drone Sound data in diverse environments. We investigated the effectiveness of state-of-the-art event Sound classification methods, i.e., a Gaussian Mixture Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), for drone Sound Detection. Our empirical results, which were obtained with a testing dataset collected on an urban street, confirmed the effectiveness of these models for operating in a real environment. In summary, our RNN models showed the best Detection performance with an F-Score of 0.8009 with 240 ms of input audio with a short processing time, indicating their applicability to real-time Detection systems.