TY - JOUR

T1 - Phase estimation of a single quasi-periodic signal

AU - Makihara, Yasushi

AU - Aqmar, Muhammad Rasyid

AU - Trung, Ngo Thanh

AU - Nagahara, Hajime

AU - Sagawa, Ryusuke

AU - Mukaigawa, Yasuhiro

AU - Yagi, Yasushi

PY - 2014/4/15

Y1 - 2014/4/15

N2 - 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, ill-posed 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.

AB - 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, ill-posed 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.

UR - http://www.scopus.com/inward/record.url?scp=84897406488&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897406488&partnerID=8YFLogxK

U2 - 10.1109/TSP.2014.2306174

DO - 10.1109/TSP.2014.2306174

M3 - Article

AN - SCOPUS:84897406488

SN - 1053-587X

VL - 62

SP - 2066

EP - 2079

JO - IEEE Transactions on Acoustics, Speech, and Signal Processing

JF - IEEE Transactions on Acoustics, Speech, and Signal Processing

IS - 8

M1 - 6740000

ER -