Real-time audio source separation by delay and attenuation compensation in the time domain

2007 ◽  
Vol 121 (2) ◽  
pp. 685
Author(s):  
Justinian Rosca
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Source Separation ◽  
Audio Source Separation ◽  
Attenuation Compensation ◽  
The Time Domain

scholarly journals A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3521 ◽  
Author(s):  
Funa Zhou ◽  
Po Hu ◽  
Shuai Yang ◽  
Chenglin Wen
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Real Time ◽  
Time Domain ◽  
Feature Fusion ◽  
Rotating Machinery ◽  
Time Domain Data ◽  
Diagnosis Method ◽  
The Time Domain ◽  
Potential Frequency

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

Convolutive Blind Source Separation Based on Wavelet De-Noising

2013 ◽  
Vol 756-759 ◽  
pp. 3356-3361 ◽  
Author(s):  
Hong Bin Zhang ◽  
Peng Fei Xu
Keyword(s):  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Noise Pollution ◽  
Processing Method ◽  
Separation System ◽  
Communication Signals ◽  
The Time Domain ◽  
Ica Algorithm ◽  
Convolutive Blind Source Separation

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

Real-time fractal signal processing in the time domain

2013 ◽  
Vol 392 (1) ◽  
pp. 89-102 ◽  
Author(s):  
András Hartmann ◽  
Péter Mukli ◽  
Zoltán Nagy ◽  
László Kocsis ◽  
Péter Hermán ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Time Domain ◽  
Fractal Signal ◽  
The Time Domain

scholarly journals Small Displacement Measuring System Based on MEMS Accelerometer

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Wei-meng Niu ◽  
Fang Li-qing ◽  
Zi-yuan Qi ◽  
De-qing Guo
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Time Domain ◽  
Moving Objects ◽  
Displacement Measurement ◽  
Measuring System ◽  
Small Displacement ◽  
Power Module ◽  
Acceleration Signal ◽  
The Time Domain

In order to intelligently measure the displacement of moving objects, a small displacement measuring system based on MEMS (microelectromechanical system) accelerometer is designed. Firstly, the principle and error of displacement measurement using acceleration signal are deeply analyzed. The method of fitting polynomial extremum with better correction effect is selected in the time domain to eliminate the trend term error caused by the DC component, which is the main source of error. Real-time correction algorithm is added to the time-domain integral of acceleration. On the hardware circuit, the measurement module, control module, storage module, and power module of the system are designed, respectively. Finally, the system-integrated packaging scheme is designed, which enables the system to complete the real-time acceleration measurement, velocity and displacement calculation, and data storage of the motion carrier. Through simulation experiments, it is found that the system has good antioverload capability, and the displacement measurement average accuracy can reach 0.96%, which has very important engineering significance.

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