A Time-frequency Cross-product Modulation Deceptive Targets Generation Based on Interrupted Sampling against WA-SAR

Author(s):  
Lixin Liu ◽  
Shiqi Xing ◽  
Bo Pang ◽  
Yongzhen Li
Keyword(s):  
2011 ◽  
Vol 54 (2) ◽  
pp. 85-102
Author(s):  
David Smallwood

A modified harmonic wavelet transform is used to estimate a time varying spectral density. The resolution of the estimate has an approximate constant time-frequency product. The estimation error is directly related to this time-frequency product. Unwanted cross product terms are effectively minimized. Several examples are given: White random, two sine waves, chirps, impulses, sums of exponentially decaying sinusoids, and a pyroshock. It is also shown how realizations can be generated from the modified harmonic wavelet transform estimate of the time varying spectral density.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki

Author(s):  
Michael Joshua Landau

Acoustical properties of speech have been shown to be related to mental states such as remission and depression. The objective of this project was to relate the energy in frequency bands with the severity of the mental state using the Beck Depression Inventory (BDI). Recorded speech was obtained from male and female subjects with mental states of remission, depression, and suicidal risk. These subjects had recorded automated and spontaneous speech samples. Multiple regression analysis was used to relate the independent energy band ratio variables with the dependent BDI scores, and thus allow the determination of equitable BDI scores for future patients. For the male group, the square of the 3rd energy band and the cross-product of the 2nd and 3rd energy band were prominent in both the reading and interviewed groups. Therefore the equation with the 2nd lowest Akaike Information Criterion (AIC) score was chosen for the reading male group, and the 1st lowest AIC score was chosen for the interviewed male group. For the female group, the square and cross-product of the 1st and 2nd energy bands were prominent in both the reading and interviewed groups. Therefore the 2nd lowest AIC score was chosen for the reading female group, and the 1st lowest AIC score was chosen for the interviewed female group. The clinician could thus determine the patient’s mood or state of mind by comparing the estimated BDI score with the ranges of total BDI scores: remitted 0 – 20, depressed 15 – 38, suicidal 38 – 46. Keywords: speech, mental states, power spectra, multiple regression, information theoretic criterion


2009 ◽  
Vol E92-B (12) ◽  
pp. 3717-3725
Author(s):  
Thomas HUNZIKER ◽  
Ziyang JU ◽  
Dirk DAHLHAUS

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