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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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Journal ArticleDOI
TL;DR: The results prove that the proposed spectrum calculation method can achieve excellent performance and has strong robustness in the condition of low signal-to-noise ratio (SNR), and the time-frequency analysis method works well in real-time.
Abstract: Radar emitter identification (REI) is significant in both military and civilian application domains. A critical step for REI is signal feature extraction. Most radar emitter signals are non-stationary, and many studies apply time-frequency spectrum features for non-stationary signal analysis in recent years. This letter proposes a novel spectrum calculation method for signal feature analysis using short-time Fourier transform (STFT) and $k$ -means algorithm. We first compute the time-frequency spectrograms of emitter signals by the proposed method. And we apply the convolutional neural network (CNN) for automatic identification based on the time-frequency images. In the experiment, we simulate different emitter signals for performance evaluation and compare our method with the spectrum analysis methods adopted in the literature. The results prove that our method can achieve excellent performance and has strong robustness in the condition of low signal-to-noise ratio (SNR), and our time-frequency analysis method works well in real-time.

22 citations

Proceedings ArticleDOI
20 Apr 2018
TL;DR: An improved prior for audio signals named instantaneous phase corrected total variation (iPCTV) is proposed, which can handle wider range of audio signals owing to the instantaneous phase correction term calculated from the observed signal.
Abstract: In optimization-based signal processing, the so-called prior term models the desired signal, and therefore its design is the key factor to achieve a good performance. For audio signals, the time-directional total variation applied to a spectrogram in combination with phase correction has been proposed recently to model sinusoidal components of the signal. Although it is a promising prior, its applicability might be restricted to some extent because of the mismatch of the assumption to the signal. In this paper, based upon the previously proposed one, an improved prior for audio signals named instantaneous phase corrected total variation (iPCTV) is proposed. It can handle wider range of audio signals owing to the instantaneous phase correction term calculated from the observed signal.

22 citations

Journal ArticleDOI
TL;DR: The usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.
Abstract: Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.

22 citations

Proceedings ArticleDOI
01 Nov 1998
TL;DR: A new transformation for discrete signals with time-varying spectra is proposed, which provides the energy density of the signal in time-frequency and a representation for the signal as well as its time- frequencies energy density.
Abstract: We propose a new transformation for discrete signals with time-varying spectra. The kernel of this transformation provides the energy density of the signal in time-frequency. With this discrete evolutionary transform we obtain a representation for the signal as well as its time-frequency energy density. To obtain the kernel of the transformation we use either the Gabor or the Malvar discrete signal representations. Signal adaptive analysis can be done using modulated or chirped bases, and implemented with either masking or image segmentation on the time-frequency plane. Different examples illustrate the implementation of the discrete evolutionary transform.

22 citations

Proceedings ArticleDOI
01 Feb 2017
TL;DR: In this article, an iterative eigenvalue decomposition of the Hankel matrix (IEVD-HM) and the Hilbert transform (HT) was proposed for time-frequency analysis of non-stationary signals.
Abstract: Non-stationary signal analysis is an essential part for many engineering fields Time-frequency analysis methods are commonly used methods for analysis of non-stationary signals In this paper, a new domain for time-frequency analysis has been proposed which has been studied for the analysis of non-stationary signals The proposed method combines two techniques namely, iterative eigenvalue decomposition of the Hankel matrix (IEVD-HM) and the Hilbert transform (HT) The IEVD-HM technique provides a set of mono-component signals where the HT has been employed to determine amplitude envelopes and instantaneous frequencies of these mono-component signals These amplitude envelope and instantaneous frequency estimations have been used to determine the time-frequency representation The obtained time-frequency representation has been studied for the analysis of synthetic non-stationary signals in order to show the effectiveness of the proposed method

22 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022338
2021253
2020229
2019261
2018320