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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TL;DR: A novel SST-based technique is proposed that can achieve more concentrated representations than RM and SST and allows for perfect signal reconstruction and the reconstructed signal has a high consistency with the general relativity proposed by Einstein.
68 citations
TL;DR: The main focus of the present paper is to study the performance of the multiclass capability of SVM techniques, and it shows an excellent prediction performance when purely time domain data is used.
67 citations
TL;DR: The Generalized State Coherence Transform (GSCT) is analyzed which is a non-linear transform of the space represented by the whole demixing matrices that enables an accurate estimation of the propagation time-delay of multiple sources in multiple dimensions.
Abstract: According to the physical meaning of the frequency-domain blind source separation (FD-BSS), each mixing matrix estimated by independent component analysis (ICA) contains information on the physical acoustic propagation related to each source and then can be used for localization purposes. In this paper, we analyze the Generalized State Coherence Transform (GSCT) which is a non-linear transform of the space represented by the whole demixing matrices. The transform enables an accurate estimation of the propagation time-delay of multiple sources in multiple dimensions. Furthermore, it is shown that with appropriate nonlinearities and a statistical model for the reverberation, GSCT can be considered an approximated kernel density estimator of the acoustic propagation time-delay. Experimental results confirm the good properties of the transform and its effectiveness in addressing multiple source TDOA detection (e.g., 2-D TDOA estimation of several sources with only three microphones).
67 citations
21 Apr 1997
TL;DR: A new four-parameter atomic decomposition of chirplets is developed for compact representation of signals with chirp components and provides a more compact and precise representation of chiral components as compared to the three- parameter ones.
Abstract: A new four-parameter atomic decomposition of chirplets is developed for compact representation of signals with chirp components. The four-parameter atom is obtained by scaling the Gaussian function, and then applying the fractional Fourier transform (FRFT), time-shift and frequency-shift operators to the scaled Gaussian. The decomposition is realized by extending the matching pursuit algorithm to four parameters. For this purpose, the four-parameter space is discretized to obtain a dense subset in the Hilbert space. Also, a related time-frequency distribution is developed for clear visualization of the signal components. The decomposition provides a more compact and precise representation of chirp components as compared to the three-parameter ones.
67 citations
TL;DR: Theoretical analysis shows that the NSTFT method is independent of the signal amplitude and is only relevant to the signal phase, thus it can be used for weak signal detection.
67 citations