Normal and hypoacoustic infant cry signal classification using time–frequency analysis and general regression neural network

2012 ◽  
Vol 108 (2) ◽  
pp. 559-569 ◽  
Author(s):  
M. Hariharan ◽  
R. Sindhu ◽  
Sazali Yaacob
Keyword(s):  
Neural Network ◽  
Frequency Analysis ◽  
Signal Classification ◽  
General Regression Neural Network ◽  
Time Frequency Analysis ◽  
Infant Cry ◽  
Time Frequency ◽  
General Regression

Time-frequency analysis-based method for application of infant cry classification

2020 ◽  
Vol 12 (2) ◽  
pp. 119
Author(s):  
J. Saraswathy ◽  
M. Hariharan ◽  
Wan Khairunizam ◽  
J. Sarojini ◽  
Sazali Yaacob
Keyword(s):  
Frequency Analysis ◽  
Time Frequency Analysis ◽  
Infant Cry ◽  
Time Frequency

Application of Time-Frequency Analysis and Back-Propagation Neural Network in the Lung Sound Signal Recognition

2012 ◽  
Vol 190-191 ◽  
pp. 927-930
Author(s):  
Mei Yung Chen ◽  
Chien Chou Huang
Keyword(s):  
Neural Network ◽  
Frequency Analysis ◽  
Recognition Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Visual Signal ◽  
Lung Sound ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Breath Sounds

In the diagnosis of the respiratory diseases, auscultation is a non-invasive and convenient diagnostic method. In the digital auscultation analysis, what method we use to analyze the lung signals which microphone recorded will affect the results of the experiment greatly. The purpose of this study is to use frequency analysis and time-frequency analysis to analyze the six lung sound signals, which are vesicular breath sounds, bronchial breath sounds, crackle, and wheeze. Finally, the study transformed the analysis results into the characteristic images, and put them to the back propagation neural network for training. After that, the study compares the results of the two methods. We also analyze the realistic lung sound signals and simulated lung sound signals, and compare the results finally. First, we use the piezoelectric microphone and data acquisition card NI-PXI 4472B to acquire LS signals, and signals preprocessing. Then we use Visual Signal to analyze the lung sound signals by time-frequency analysis. We also analyze the lung sound signals which are from the auscultation teaching website. Finally we compare the result of two kinds of signals, and assess their similarity and accuracy by the test of back-propagation neural network. According to the result of this study, we found that time-frequency analysis provide much information about the lung signals, and are more suitable as a basis of diagnosis, and increase the recognition rate of the back-propagation neural network.

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