scholarly journals Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation

10.2196/16746 ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. e16746
Author(s):  
Chi-Te Wang ◽  
Ji-Yan Han ◽  
Shih-Hau Fang ◽  
Ying-Hui Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Background Noise ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Detection Accuracy ◽  
Real Time Processing ◽  
Speech Detection ◽  
Reduction Function

Background Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. Objective This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. Methods We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. Results The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. Conclusions This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

scholarly journals Ambulatory Phonation Monitoring With Wireless Microphones Based on the Speech Energy Envelope: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chi-Te Wang ◽  
Ji-Yan Han ◽  
Shih-Hau Fang ◽  
Ying-Hui Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Background Noise ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Detection Accuracy ◽  
Real Time Processing ◽  
Speech Detection ◽  
Reduction Function

BACKGROUND Voice disorders mainly result from chronic overuse or abuse, particularly in occupational voice users such as teachers. Previous studies proposed a contact microphone attached to the anterior neck for ambulatory voice monitoring; however, the inconvenience associated with taping and wiring, along with the lack of real-time processing, has limited its clinical application. OBJECTIVE This study aims to (1) propose an automatic speech detection system using wireless microphones for real-time ambulatory voice monitoring, (2) examine the detection accuracy under controlled environment and noisy conditions, and (3) report the results of the phonation ratio in practical scenarios. METHODS We designed an adaptive threshold function to detect the presence of speech based on the energy envelope. We invited 10 teachers to participate in this study and tested the performance of the proposed automatic speech detection system regarding detection accuracy and phonation ratio. Moreover, we investigated whether the unsupervised noise reduction algorithm (ie, log minimum mean square error) can overcome the influence of environmental noise in the proposed system. RESULTS The proposed system exhibited an average accuracy of speech detection of 89.9%, ranging from 81.0% (67,357/83,157 frames) to 95.0% (199,201/209,685 frames). Subsequent analyses revealed a phonation ratio between 44.0% (33,019/75,044 frames) and 78.0% (68,785/88,186 frames) during teaching sessions of 40-60 minutes; the durations of most of the phonation segments were less than 10 seconds. The presence of background noise reduced the accuracy of the automatic speech detection system, and an adjuvant noise reduction function could effectively improve the accuracy, especially under stable noise conditions. CONCLUSIONS This study demonstrated an average detection accuracy of 89.9% in the proposed automatic speech detection system with wireless microphones. The preliminary results for the phonation ratio were comparable to those of previous studies. Although the wireless microphones are susceptible to background noise, an additional noise reduction function can alleviate this limitation. These results indicate that the proposed system can be applied for ambulatory voice monitoring in occupational voice users.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

scholarly journals A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Dhanalakshmi Krishnan Sadhasivan ◽  
Kannapiran Balasubramanian
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Accuracy ◽  
The Real ◽  
Common Path ◽  
Research Areas ◽  
Water Pipeline ◽  
Active Research

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.

scholarly journals Application of deep learning in automatic detection of technical and tactical indicators of table tennis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0245259
Author(s):  
Fufeng Qiao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Short Term Memory ◽  
Detection System ◽  
Minimum Mean Square Error ◽  
Automatic Detection ◽  
Table Tennis ◽  
Time Dynamic ◽  
Time Motion ◽  
Automatic Detection System

A DCNN-LSTM (Deep Convolutional Neural Network-Long Short Term Memory) model is proposed to recognize and track table tennis’s real-time trajectory in complex environments, aiming to help the audiences understand competition details and provide a reference for training enthusiasts using computers. Real-time motion features are extracted via deep reinforcement networks. DCNN tracks the recognized objects, and the LSTM algorithm predicts the ball’s trajectory. The model is tested on a self-built video dataset and existing systems and compared with other algorithms to verify its effectiveness. Finally, an overall tactical detection system is built to measure ball rotation and predict ball trajectory. Results demonstrate that in feature extraction, the Deep Deterministic Policy Gradient (DDPG) algorithm has the best performance, with a maximum accuracy rate of 89% and a minimum mean square error of 0.2475. The accuracy of target tracking effect and trajectory prediction is as high as 90%. Compared with traditional methods, the performance of the DCNN-LSTM model based on deep learning is improved by 23.17%. The implemented automatic detection system of table tennis tactical indicators can deal with the problems of table tennis tracking and rotation measurement. It can provide a theoretical foundation and practical value for related research in real-time dynamic detection of balls.

scholarly journals Improving Wavelet Threshold De-noising Applied on Parts Detection

2018 ◽  
Vol 232 ◽  
pp. 04084
Author(s):  
Lizhou Cheng ◽  
Jian Mao ◽  
Hongyuan Wei
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Image Features ◽  
Image Feature ◽  
Threshold Function ◽  
Image Noise Reduction ◽  
The Impact

The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.

Defects Detection of Printed Circuit Board Based on the Machine Vision Method

2014 ◽  
Vol 494-495 ◽  
pp. 785-788 ◽  
Author(s):  
Wen Bin Wang ◽  
Dao Yuan Liu ◽  
Yu Qin Yao
Keyword(s):  
Image Processing ◽  
Image Registration ◽  
Real Time ◽  
Printed Circuit Board ◽  
Detection Efficiency ◽  
Detection System ◽  
Circuit Board ◽  
Algorithm Analysis ◽  
Real Time Processing ◽  
Morphological Image Processing

The processing of target image using image processing technology, can realize the non-contact online detecting circuit board, thus greatly improve the detection efficiency, reducing the defective rate. This paper provides the detection system based on the methods of pre-processing the standard circuit board image and the circuit board image to be detected, two value segmentation, morphological image processing, image registration and poor shadow detection processing, among them ,image registration is the key. In order to improve the processing speed to achieve real-time processing, image registration using rapid processing algorithm. Analysis of the experimental results, the method can detect the defects on the circuit board to be detected accurately, and can achieve the automatic real-time detection purposes.

Reach on Wireless Sensor Networks Security Issues and IDS Model

2014 ◽  
Vol 530-531 ◽  
pp. 45-49
Author(s):  
Jian Gang Tang
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Detection System ◽  
Detection Accuracy ◽  
Security Measures ◽  
Security Technology ◽  
Single Node ◽  
Security Issues ◽  
Network Intrusion ◽  
Wsn Security

Security measures could not absolutely prevent network intrusion. The security technology of intrusion detection system had made up for the lack of preventive measures; it could provide real-time intrusion detection and take appropriate protection for network. The research directions of WSN security were how to improve security strength and prolong the life of nodes, how to enhance the preventive ability of intelligent security system and real-time detection with high detection accuracy. This paper analyzed the typical network intrusion and defensive strategies, and researched WSN intrusion detection model by analyzing the typical algorithm. IDS model was divided into three types the first was based on single-node detection, the other was based on Multi-node peer cooperative, and the third was based on task decomposition level. Finally the paper gave the main research topic and direction for WSN security issues.

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