Topic
Time–frequency analysis
About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.
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14 Oct 1996
TL;DR: Three main time-frequency/time-scale (TF/TS) analysis algorithms-windowed Fourier transform (WFT), Wigner-Ville distribution (WVD), and wavelet analysis (WA), have been studied and implemented and compared.
Abstract: In this research, three main time-frequency/time-scale (TF/TS) analysis algorithms-windowed Fourier transform (WFT), Wigner-Ville distribution (WVD), and wavelet analysis (WA), have been studied and implemented. We compare the different algorithms using an identical set of synthetic signals, from which one can realize their individual features. To justify the effectiveness and to highlight the requisite of using TF/TS analysis, two test-benches have been conducted and investigated. One is to analyze transient vibration signals measured on the end-effector of an industrial robot during plane motion and the other is to analyse the nonstationary vibration response of a rotor-dynamic system with both an electro-mechanical clutch and brake.
29 citations
TL;DR: Experimental results with real underwater acoustic data and land acoustic data, as well as Monte Carlo simulation results with computer-generated data, have confirmed the veracity and practical usefulness of the estimation scheme advocated.
Abstract: New results on the recently introduced steady-motion-based Dopplerlet transform is presented and an estimation scheme for both range and speed of moving sound source using Dopplerlet transform is considered. First, we derive the real, discrete, and real discrete Dopplerlet transforms from their complex counterpart. Second, we reformulate some of the existing integral transformations within the framework of the Dopplerlet transform. Finally, we provide the application results. Experimental results with real underwater acoustic data and land acoustic data, as well as Monte Carlo simulation results with computer-generated data, have confirmed the veracity and practical usefulness of the estimation scheme advocated.
29 citations
23 Mar 1992
TL;DR: It is shown that the frequency dual to dechirping exists, so that all of the time-frequency plane projections can be calculated efficiently.
Abstract: The Radon transform of a time-frequency distribution produces local areas of signal concentration that facilitate interpretation of multicomponent signals. The Radon transform can be efficiently implemented with dechirping in the time domains; however, only half of the possible projections through the time-frequency plane can be realized because of aliasing. It is shown that the frequency dual to dechirping exists, so that all of the time-frequency plane projections can be calculated efficiently. Some Radon transforms of Wigner distributions are demonstrated. >
29 citations
TL;DR: A threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates, and achieves improved identification accuracy and robustness against higher levels of noise and the offset problem.
Abstract: The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.
29 citations
TL;DR: A PD signal separation algorithm based on extraction of high level image features that is effective for identifying the number of active sources and separating mixed PD signals initiated from multiple sources is presented.
Abstract: Transformers are known as one of the most important equipment in power system transmission and distribution networks. Partial Discharge (PD) measurement and PRPD interpretation is a powerful tool to monitor the situation and evaluating the risk of power transformer failure. Multiple discharge sources affect the accuracy of interpretation of PRPD. This paper presents a PD signal separation algorithm based on extraction of high level image features. The time-frequency S transform (ST) is applied to the PD signal waveforms, acquired by digital detection instruments at 100 MS/s. The resultant ST matrix is then converted to gray scale image from which high level features are extracted using Bag of Words (BoW). Principle component analysis (PCA) transform is applied to BoW feature to reduce the dimension of features. Calinski-Harabasz criterion is calculated to identify the number of active sources and then Gaussian mixture model (GMM) clustering is used to discover clusters in the feature space. The proposed separation algorithm is examined with mixed current impulse signals acquired from PD experiments on artificial multi-defect models. The separation results indicate that the proposed algorithm is effective for identifying the number of active sources and separating mixed PD signals initiated from multiple sources.
29 citations