We introduce novel methods to determine optimum detection thresholds for the Progressive Multi-Channel Correlation (PMCC) algorithm used by the International Data Centre (IDC) to perform infrasound and seismic station...We introduce novel methods to determine optimum detection thresholds for the Progressive Multi-Channel Correlation (PMCC) algorithm used by the International Data Centre (IDC) to perform infrasound and seismic station-level nuclear-event detection. Receiver Operating Characteristic (ROC) curve analysis is used with real ground truth data to determine the trade-off between the probability of detection (PD) and the false alarm rate (FAR) at various detection thresholds. Further, statistical detection theory via maximum a posteriori and Bayes cost approaches is used to determine station-level optimum “family” size thresholds before detections should be considered for network-level processing. These threshold-determining methods are extensible for family-characterizing statistics other than “size,” such as a family’s collective F-statistic or signal-to-noise ratio (SNR). Therefore, the reliability of analysts’ decisions as to whether families should be preserved for network-level processing can only benefit from access to multiple, independent, optimum decision thresholds based upon size, F-statistic, SNR, etc.展开更多
In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network moni...In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network monitoring and keywords spotting. In the part of network monitoring, this paper adopts a method which is based on ARP spoofing technology to monitor the users＇ data, and to obtain the original audio streams. In the part of keywords spotting, the extraction methods of PLP （one of the main characteristic arameters） is studied, and improved feature parameters- PMCC are put forward. Meanwhile, in order to accurately detect syllable, the paper the double-threshold method with variance of frequency band method, and use the latter to carry out endpoint detection. Finally, keywords recognition module is built by HMM, and identification results are contrasted under Matlab environment. From the experiment results, a better solution for the application of key words recognition technology in network monitoring is found.展开更多
文摘We introduce novel methods to determine optimum detection thresholds for the Progressive Multi-Channel Correlation (PMCC) algorithm used by the International Data Centre (IDC) to perform infrasound and seismic station-level nuclear-event detection. Receiver Operating Characteristic (ROC) curve analysis is used with real ground truth data to determine the trade-off between the probability of detection (PD) and the false alarm rate (FAR) at various detection thresholds. Further, statistical detection theory via maximum a posteriori and Bayes cost approaches is used to determine station-level optimum “family” size thresholds before detections should be considered for network-level processing. These threshold-determining methods are extensible for family-characterizing statistics other than “size,” such as a family’s collective F-statistic or signal-to-noise ratio (SNR). Therefore, the reliability of analysts’ decisions as to whether families should be preserved for network-level processing can only benefit from access to multiple, independent, optimum decision thresholds based upon size, F-statistic, SNR, etc.
基金supported by the Natural Science Foundation of Guangxi Province(No.60961002)
文摘In this paper, the specific application of key words Spotting used in the network monitoring is studied, and the keywords spotting is emphasized. The whole monitoring system is divided into two mod-ules： network monitoring and keywords spotting. In the part of network monitoring, this paper adopts a method which is based on ARP spoofing technology to monitor the users＇ data, and to obtain the original audio streams. In the part of keywords spotting, the extraction methods of PLP （one of the main characteristic arameters） is studied, and improved feature parameters- PMCC are put forward. Meanwhile, in order to accurately detect syllable, the paper the double-threshold method with variance of frequency band method, and use the latter to carry out endpoint detection. Finally, keywords recognition module is built by HMM, and identification results are contrasted under Matlab environment. From the experiment results, a better solution for the application of key words recognition technology in network monitoring is found.
