Showing papers on "Reassignment method published in 2008"

Estimation of Frequency for AM/FM Models Using the Phase Vocoder Framework

Abstract: This paper proposes an extension of the applicability of phase-vocoder-based frequency estimators for generalized sinusoidal models, which include phase and amplitude modulations. A first approach, called phase corrected vocoder (PCV), takes into account the modification of the Fourier phases resulting from these modulations. Another approach is based on an adaptation of the principles of the time-frequency reassignment and is referred to as the reassigned vocoder (RV). The robustness of the estimation against noise is studied, both theoretically and experimentally, and the performance is assessed in comparison with two state-of-the-art algorithms: an unmodified version of the reassignment method and a quadratically interpolated fast Fourier transform method (QIFFT).

A new reassigned time-frequency representation

Abstract: In this paper, the reassignment method is applied to the local polynomial periodogram to improve the readability of the time-frequency representation. Some interesting properties of the reassigned local polynomial periodogram are demonstrated. Simulations are presented to show that the reassigned local polynomial periodogram can improve the readability of the time-frequency representation, compared to the reassigned spectrogram and reassigned smoothed pseudo Wigner-Ville distribution.

A Time-Frequency Analysis Method Using Stochastic Resonance

Abstract: Time-frequency analysis has been widely used in non-stationary signal analysis, and the reassignment method can improve the performance of the time-frequency analysis. But they can not analyze signals at low signal-to-noise ratio. The stochastic resonance system can increase signal-to-noise ratio. Using this feature this paper proposes a time-frequency analysis method applying stochastic resonance as a pre-processing system. It means that a signal is pre-processed by stochastic resonance system first, and then is analyzed using time-frequency distribution and reassignment method. The simulation results show that the proposed method can analyze signals very well under low signal-to-noise ratio. When signal-to-noise ratio is -11dB, the reassignment spectrogram using stochastic resonance provides a higher time-frequency concentrate and improves the readability of spectrogram; the reassignment scalogram using stochastic resonance has the good performance.