Performance of real-time source-location estimators for a large-aperture microphone array

2005 ◽  
Vol 13 (4) ◽  
pp. 593-606 ◽  
Author(s):  
H.F. Silverman ◽  
Ying Yu ◽  
J.M. Sachar ◽  
W.R. Patterson
Keyword(s):  
Real Time ◽  
Microphone Array ◽  
Source Location ◽  
Large Aperture

Study of Real-Time Monitoring System on the Underground Mining Activities Based on Explosion Source Location Technology

2012 ◽  
Vol 190-191 ◽  
pp. 1179-1182
Author(s):  
Xiu Zhi Meng ◽  
Zeng Zhi Zhang ◽  
Zong Sheng Wang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Underground Mining ◽  
Coal Mines ◽  
Source Location ◽  
Environmental Damage ◽  
Mining Activities ◽  
Real Time Monitoring ◽  
P Waves ◽  
Vibration Sensors

The mining boundary ultra-layer & cross-border of some small coal mines in the profit-driven results in a many of safety accidents, waste of resources and environmental damage while the state can not achieve the full uninterrupted supervision because of the backward monitoring tools and equipment. In this situation the real-time monitoring system for underground mining activities is designed based on explosion source location technology. Small and medium-sized coal mines tunnel by blasting operations. The P waves are picked up by acceleration vibration sensors buried underground that are identified and dealt by using wavelet transform. The bursting point is located by the Geiger algorithm and displayed in the mine’s electronic map. The monitor system has good stability, small positioning error by field-proven.

scholarly journals Rotating equipment fault noise location based on adaptive beamforming algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012042
Author(s):  
Yongshao Xu ◽  
Bingzheng Liu ◽  
Haotian Shang ◽  
Mingduo Wang
Keyword(s):  
Microphone Array ◽  
State Parameter ◽  
Noise Signal ◽  
Source Location ◽  
Adaptive Beamforming ◽  
Rotating Machinery ◽  
Acoustic Sensor ◽  
Unsteady State ◽  
Beamforming Algorithm ◽  
Fault Source

Abstract Rotating machinery often produces continuous impact during operation due to the change of load and speed, which shows the characteristics of unsteady state and time-varying. Its working state can not be comprehensively judged by a single vibration state parameter. Therefore, this paper proposes to use acoustic sensors to collect the fault noise signal of rotating machinery, and use the whole column of sensors to detect the fault noise signal. Based on the microphone array, this paper studies the adaptive beamforming algorithm (MVDR) to locate the fault source of rotating machinery in space. The effect of fault source location is verified by simulation and equipment measurement experiments. The acoustic sensor does not in contact with the equipment, which will not damage the generator set, but also provide more effective information for fault source location and fault diagnosis and analysis.

scholarly journals A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3527
Author(s):  
Ching-Feng Liu ◽  
Wei-Siang Ciou ◽  
Peng-Ting Chen ◽  
Yi-Chun Du
Keyword(s):  
Real Time ◽  
Speech Processing ◽  
Microphone Array ◽  
Speech Sound ◽  
Separation Method ◽  
Supervised Machine Learning ◽  
Machine Language ◽  
Speech Separation ◽  
Real Time System ◽  
Stationary Noise

In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.

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