New Approach to Speech Signal Recognition Using Nonlinear Signal Decomposition by Measuring Wiener Kernels

2002 ◽  
Vol 4 (4) ◽  
pp. 265-276
Author(s):  
Alexander M. Krot ◽  
Boris A. Goncharov ◽  
Polina P. Tkachova
Keyword(s):  
Speech Signal ◽  
Signal Decomposition ◽  
Signal Recognition ◽  
New Approach ◽  
Wiener Kernels ◽  
Nonlinear Signal

Detection of the Third Heart Sound Based on Nonlinear Signal Decomposition and Time–Frequency Localization

2016 ◽  
Vol 63 (8) ◽  
pp. 1718-1727 ◽  
Author(s):  
Shovan Barma ◽  
Bo-Wei Chen ◽  
Wen Ji ◽  
Seungmin Rho ◽  
Chih-Hung Chou ◽  
...  
Keyword(s):  
Heart Sound ◽  
Signal Decomposition ◽  
Time Frequency ◽  
The Third ◽  
Third Heart Sound ◽  
Time Frequency Localization ◽  
Nonlinear Signal

Decomposition of T2 Signal for Multivariate Assembly Process Control Using Bayesian Network

2011 ◽  
Vol 314-316 ◽  
pp. 2370-2374
Author(s):  
Yin Hua Liu ◽  
Yang Yang
Keyword(s):  
Bayesian Network ◽  
Control Charts ◽  
Mean Shift ◽  
Signal Decomposition ◽  
Assembly Process ◽  
New Approach ◽  
Root Cause ◽  
Monitoring And Diagnosis ◽  
The Mean ◽  
Interaction Change

The process monitoring and diagnosis in assembly process is important. Multivariate T2 control charts are applied to detect the mean shift and interaction change in the assembly process. However, T2 charts can not identify the root cause of the change. The traditional MTY method for T2 signal decomposition is computationally expensive, especially when the dimension of the variables is high. A new approach based on Bayesian network to identify the significant cause of T2 signals is proposed in this paper. The headlamp bracket case is used to illustrate the overall procedure. And the effectiveness of the proposed approach is evaluated.

Application of Local Wave Method in the Structural Health Monitoring Signal Decomposition

2013 ◽  
Vol 457-458 ◽  
pp. 969-973
Author(s):  
Lin Yang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Response ◽  
Signal Decomposition ◽  
Experimental Simulation ◽  
Wave Method ◽  
Structural Health ◽  
Bridge Health Monitoring ◽  
Local Wave ◽  
Nonlinear Signal

Health monitoring of the bridge structure has gradually become one of the hot topics. The signal decomposition technology is the key technique of the bridge structural health monitoring. The traditional data analysis and processing methods, which can only be applied to stationary or linear signal processing, have significant limitations. However, the structural response signals tested are mostly non-stationary and nonlinear. So methods that can effectively analyze non-stationary and nonlinear signal are urgently needed. Based on the summarization and analysis of the shortage of wavelet analysis method, the application of local wave method for data processing and analysis in structural health monitoring is put forward. The feasibility and superiority of local wave method is discussed. Experimental simulation results show that the application of local wave method in bridge health monitoring signal decomposition is feasible.

scholarly journals Research on Speech Recognition Method in Multi Layer Perceptual Network Environment

2021 ◽  
Vol 15 ◽  
pp. 996-1004
Author(s):  
Kai Zhao ◽  
Dan Wang
Keyword(s):  
Speech Recognition ◽  
Speech Signal ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Average Energy ◽  
Signal Recognition ◽  
Network Environment ◽  
Recognition Method ◽  
Zero Crossing ◽  
Zero Crossing Rate

Aiming at the problem of low recognition rate in speech recognition methods, a speech recognition method in multi-layer perceptual network environment is proposed. In the multi-layer perceptual network environment, the speech signal is processed in the filter by using the transfer function of the filter. According to the framing process, the speech signal is windowed and framing processed to remove the silence segment of the speech signal. At the same time, the average energy of the speech signal is calculated and the zero crossing rate is calculated to extract the characteristics of the speech signal. By analyzing the principle of speech signal recognition, the process of speech recognition is designed, and the speech recognition in multi-layer perceptual network environment is realized. The experimental results show that the speech recognition method designed in this paper has good speech recognition performance

New approach in quantification of emotional intensity from the speech signal: emotional temperature

2015 ◽  
Vol 42 (24) ◽  
pp. 9554-9564 ◽  
Author(s):  
Jesús B. Alonso ◽  
Josué Cabrera ◽  
Manuel Medina ◽  
Carlos M. Travieso
Keyword(s):  
Speech Signal ◽  
Emotional Intensity ◽  
New Approach
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