Audio fingerprinting based on analyzing time-frequency localization of signals

2004 ◽  
Author(s):  
Chun-Shien Lu
Keyword(s):  
Time Frequency ◽  
Audio Fingerprinting ◽  
Time Frequency Localization

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

scholarly journals Orthogonal-based wavelet analysis of wind turbulence and correlation between turbulence and forces

2007 ◽  
Vol 29 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Le Thai Hoa ◽  
Nguyen Dong Anh
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Existing Problems ◽  
Physical Measurements ◽  
Force Relation ◽  
Wind Turbulence ◽  
Time Frequency Localization ◽  
Physical Simulations ◽  
Wavelet Decompositions

Recent models of wind turbulence and turbulence-force relation as well still contain uncertainties. Further studies on them are needed to gain the better knowledge to refine the existing problems from analytical computations to wind tunnel's physical simulations in the wind engineering. The continuous and discrete wavelet transforms have been applied as powerful transformation tools to represent time series into the time-frequency localization. This paper will apply the orthogonal-based wavelet decomposition to investigate the intermittency of the turbulence and to detect the turbulence-force correlation in the both temporal-spectral information using proposed cross energy of wavelet decompositions. Analyzing data have been obtained by physical measurements on model from the wind tunnel tests.

Voltage Flicker Extraction Based on Wavelet Analysis

2013 ◽  
Vol 385-386 ◽  
pp. 1389-1393 ◽  
Author(s):  
Lin Chai ◽  
Jun Ru Sun
Keyword(s):  
Low Frequency ◽  
Matlab Simulation ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Voltage Signal ◽  
Time Frequency Localization ◽  
Resolution Analysis ◽  
Modulus Maximum ◽  
Voltage Flicker ◽  
Non Stationary Signal

Extracting voltage flicker from the sampling voltage signal is a precondition for management of flicker. Voltage flicker signal is a low frequency time-varying non-stationary signal. The traditional fourier transform has great limitations when analyze the non-stationary signal for not having the time resolution. As wavelet transform has good property of time-frequency localization, it become a powerful tool for analyze this kind of signal. This paper adopts multi-resolution analysis of wavelet to extract voltage flicker signal. Furthermore, according to the characteristics of wavelet function, this paper selects Daubechies wavelet to accomplish the multi-level decomposition and reconstruction of signal, in order to get the frequency and amplitude of voltage flicker signals. Based on the principle of modulus maximum, it can be determined what time the voltage flicker happen and over. The results of MATLAB simulation indicate that voltage flicker signal can be effectively extracted by wavelet multi-resolution analysis. Wavelet multi-resolution analysis is considerably ideal for voltage flicker extraction.

The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

2012 ◽  
Vol 452-453 ◽  
pp. 782-788
Author(s):  
Jin Feng Wang ◽  
Li Jie Feng ◽  
Zhao Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Coal Mine ◽  
Evaluation Model ◽  
Wavelet Neural Network ◽  
Time Frequency ◽  
Model Based ◽  
Time Frequency Localization ◽  
Mine Flooding ◽  
Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

Daubechies Wavelets Based Robust Audio Fingerprinting for Content-Based Audio Retrieval

2012 ◽  
Vol 4 (2) ◽  
pp. 49-69
Author(s):  
Wei Sun ◽  
Zhe-Ming Lu ◽  
Fa-Xin Yu ◽  
Rong-Jun Shen
Keyword(s):  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Experimental Results ◽  
Daubechies Wavelet ◽  
Linear Speed ◽  
Time Frequency ◽  
Audio Fingerprinting ◽  
Audio Retrieval ◽  
Audio Clip ◽  
Frequency Features

Audio fingerprinting is the process to obtain a compact content-based signature that summarizes the essence of an audio clip. In general, existing audio fingerprinting schemes based on wavelet transforms are not robust against large linear speed changes. The authors present a novel framework for content-based audio retrieval based on the audio fingerprinting scheme that is robust against large linear speed changes. In the proposed scheme, 8 levels Daubechies wavelet decomposition is adopted for extracting time-frequency features and two fingerprint extraction algorithms are designed. The experimental results from this study are discussed further into the article.

Local Price uncertainty principle and time-frequency localization operators for the Hankel–Stockwell transform

2020 ◽  
Vol 18 (06) ◽  
pp. 2050050
Author(s):  
Nadia Ben Hamadi ◽  
Zineb Hafirassou
Keyword(s):  
Uncertainty Principle ◽  
Price Uncertainty ◽  
Localization Operators ◽  
Von Neumann ◽  
Time Frequency ◽  
Neumann Class ◽  
Time Frequency Localization ◽  
Stockwell Transform

For the Hankel–Stockwell transform, the Price uncertainty principle is proved, we define the Localization operators and we study their boundedness and compactness. We also show that these operators belong to the so-called Schatten–von Neumann class.

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