2003Unpublished venueRequires access

A new time-frequency analysis based upon AR model

Wang Haijun, Liu Guizhong, Fan Wanchun

Open publisher page 6 citations

Abstract

In this paper we analyzed the reasons why the discrete Wigner-Ville-distribution (WVD) of real-valued signal sampled at the Nyquist rate has spectral aliasing, whereas short time Fourier transform (STFT) has not such problems. For the time-frequency resolution of STFT spectrogram is very poor, a novel method of time-frequency analysis based on auto-regressive model (AR) is presented in this paper, which inherits merits of STFT spectrogram and has very good time-frequency resolution. When data for processing are very large, the new method may have excellent performance for promoting velocity of calculating, saving storage and keeping high time-frequency resolution. In addition, the applications of the new method were also illustrated for identifying ripple-fired explosions, the results were compared with that of spectrogram. Experiments showed that the performances of the new algorithm were superior than that of spectrogram.

About this research paper

What this paper is about

In this paper we analyzed the reasons why the discrete Wigner-Ville-distribution (WVD) of real-valued signal sampled at the Nyquist rate has spectral aliasing, whereas short time Fourier transform (STFT) has not such problems. For the time-frequency resolution of STFT spectrogram is very poor, a novel method of time-frequency analysis based on auto-regressive model (AR) is presented in this paper, which inherits merits of STFT spectrogram and has very good time-frequency resolution. When data for processing are very large, the new method may have excellent performance for promoting velocity of calculating, saving storage and keeping high time-frequency resolution. In addition, the applications of the new method were also illustrated for identifying ripple-fired explosions, the results were compared with that of spectrogram. Experiments showed that the performances of the new algorithm were superior than that of spectrogram.

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OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper we analyzed the reasons why the discrete Wigner-Ville-distribution (WVD) of real-valued signal sampled at the Nyquist rate has spectral aliasing, whereas short time Fourier transform (STFT) has not such problems. For the time-frequency resolution of STFT spectrogram is very poor, a novel method of time-frequency analysis based on auto-regressive model (AR) is presented in this paper, which inherits merits of STFT spectrogram and has very good time-frequency resolution. When data for processing are very large, the new method may have excellent performance for promoting velocity of calculating, saving storage and keeping high time-frequency resolution. In addition, the applications of the new method were also illustrated for identifying ripple-fired explosions, the results were compared with that of spectrogram. Experiments showed that the performances of the new algorithm were superior than that of spectrogram.

Key concepts: Spectrogram, Short-time Fourier transform, Time–frequency analysis, Aliasing, Computer science, Speech recognition, Algorithm, Wigner distribution function

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