Estimation of quasiperiodic signal parameters by means of dynamic signal models

Gruber, Péter; Tödtli, J.

doi:10.1109/78.277847

Cited by 24 publications

(8 citation statements)

References 3 publications

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“…This parametric expression is widely used in the context of periodic signal reconstruction [14] and detection [15], enhancement of a specific frequency [16], estimation of frequency [17], estimation of amplitude [18], estimation of phase [19], [20], phase synchrony [21], and decomposition of multiple periodic signals [22]- [24].…”

Section: Related Workmentioning

confidence: 99%

Phase Estimation of a Single Quasi-Periodic Signal

Makihara

Aqmar

Trung

et al. 2014

IEEE Trans. Signal Process.

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We propose a method for phase estimation of a single non-parametric quasi-periodic signal. Assuming signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-periodic signal and a signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, illposed because of combination ambiguity between the phase evolution and the normalized periodic signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-periodic signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Phase Estimation of a Single Quasi-Periodic Signal

Makihara

Aqmar

Trung

et al. 2014

IEEE Trans. Signal Process.

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“…In order to overcome this problem, the NFT (Notch Fourier Transform), deriving the Fourier coefficients by a sliding procedure [3], and CNFT (Constrained Notch Fourier Transform), improving the noise immunity of the NFT [4], have been proposed. To use an adaptive approach, the LMS (Least Mean Square) algorithm method (LMS method) [5,6] and methods using a Kalman filter [7] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a Fourier coefficient estimation method using IIR‐BPFs and an LMS algorithm and improvement of its performance

Kudoh

Tadokoro

2002

Electron Comm Jpn Pt III

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SUMMARYIn many areas, it is extremely important to accurately estimate the amplitude and phase of a signal even if the frequency contained in the signal to be analyzed is known. For cases in which the signal frequencies to be analyzed are in a nonharmonic relation and white noise and interference are superposed on the signal, a method (BPLMS method) combining the IIR type BPF and the LMS algorithm has been proposed to derive the Fourier coefficients of the signal components accurately. In the present paper, in order to investigate a method of improving the BPLMS method, an approximate equation for the estimated accuracy is derived. Also, the validity of the approximate equation is confirmed by computer simulations under various conditions. As a result of the approximate analysis, it is shown that the normalized frequency difference between the signal and the interference can be increased by combining a downsampling process so that the estimation accuracy can be improved. Finally, by simulation, the proposed method and the BPLMS method are compared in terms of estimation accuracy. An example of application to the pitch estimation of musical sounds is given.

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“…Such parametric expression is widely used in the context of periodic signal reconstruction [7] and detection [8], enhancement of a specific frequency [9], estimation of amplitude [10], and decomposition of multiple periodic signals [11] [12] [13]. The common key technique in these approaches is parameter estimation and hence, non-parametric periodic signals are out of scope.…”

Section: Related Workmentioning

confidence: 99%

Phase Registration of a Single Quasi-Periodic Signal Using Self Dynamic Time Warping

Makihara

Trung

Nagahara

et al. 2011

Computer Vision – ACCV 2010

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Abstract. This paper proposes a method for phase registration of a single non-parametric quasi-periodic signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-periodic signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiperiodic signals from both simulated and real data show the effectiveness of the proposed method.

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Estimation of quasiperiodic signal parameters by means of dynamic signal models

Cited by 24 publications

References 3 publications

Phase Estimation of a Single Quasi-Periodic Signal

Phase Estimation of a Single Quasi-Periodic Signal

Performance analysis of a Fourier coefficient estimation method using IIR‐BPFs and an LMS algorithm and improvement of its performance

Phase Registration of a Single Quasi-Periodic Signal Using Self Dynamic Time Warping

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