Signal Processing - May 2017 - 106

101
100
T-SRC (500)
T-SRC (300)
T-SRC (100)
F-SRC (Uncalibrated)
F-SRC (Calibrated)

Time/s

10-1
10-2
10-3
10-4
10-5

128
256
512
Length of the Input Signal

1,028

Figure 5. The computation time for time-domain SRC (T-SRC), uncalibrated, and calibrated
frequency-domain SRC (F-SRC).

method requires significantly more computation time than the other two methods.
It should be noted that utilizing other
optimization techniques can speed up the
optimization, which is out of the scope
of this article. Remarkably, the proposed
method is particularly useful for applications where higher conversion accuracies
are most desirable.

Conclusions
In this article, we present a method for
calibrating frequency-domain SRC to
significantly minimize conversion errors.
This method can be formulated as a sparsity regularized problem whose solution
has been widely available in the literature.
In addition, to effectively decrease the
amplitude MSE of the time-domain SCR
output, the proposed method also desirably reduces the phase error of the SRC
output in the DFT domain.

Acknowledgment
This research work is partially supported
by the National Natural Science Foundation of China (number 61571174) and the
Zhejiang Provincial Natural Science Foundation of China (number LY15F010010).
Xiumei Li is the corresponding author of
this article.

Authors
Lifan Zhao (zhao0145@e.ntu.edu.sg)
received his B.Sc. degree in electronic
engineering from Xidian University,
106

China, in 2010 and the Ph.D. degree
from the School of Electrical and
Electronic Engineering, Nanyang
Technological University (NTU), Singapore, in 2016. He is currently a research
fellow with NTU. His research interests include statistical signal processing, machine learning, compressed
sensing, and Bayesian methods and
their applications.
Xiumei Li (lixiumei@pmail.ntu
.edu.sg) received her B.Sc. degree in
electrical engineering from Lanzhou
University, China, in 1999, her M.Sc.
degree in information and signal processing from the Institute of Acoustics,
Chinese Academy of Sciences, in 2002,
and the Ph.D. degree in information engineering from Nanyang Technological
University, Singapore, in 2010. She is
currently an associate professor with the
School of Information Science and Engineering, Hangzhou Normal University,
China. Her research interests include
time-frequency analysis and its applications as well as compressive sensing
and its applications.
Lu Wang (wanglu@nwpu.edu.cn)
received her B.Sc. degree in electronic engineering from Xidian University in 2007 and
M.Eng. degree in signal processing from
the National Key Lab of Radar Signal
Processing, Xidian University, China, in
2010. She obtained her Ph.D. degree in
2014 from the School of Electrical and
Electronic Engineering, Nanyang TechIEEE Signal Processing Magazine

May 2017

nological University, Singapore. She is currently an assistant professor with the School
of Marine Science and Technology,
Northwestern Polytechnical University,
Xi'an, China. Her major research interests
include sparse signal processing and array
and sonar signal processing.
Guoan Bi (egbi@ntu.edu.sg) received
his B.Sc. degree in radio communications, Dalian University of Technology,
China, in 1982. He received his M.Sc.
degree in telecommunication systems
and his Ph.D. degree in electronics systems from Essex University, United
Kingdom, in 1985 and 1988, respectively. Between 1988 and 1990, he was a
research fellow at the University of
Surrey, United Kingdom. Since 1991, he
has been with the School of Electrical
and Electronic Engineering, Nanyang
Technological University, Singapore. He
was the chair of the IEEE Signal Processing Society, Singapore Chapter. His
research interests include digital signal
processing algorithms and hardware
structures and signal processing for various applications including sonar, radar,
and communications. He is a Senior
Member of the IEEE.

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Table of Contents for the Digital Edition of Signal Processing - May 2017