TY - GEN
T1 - DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION
AU - Angelou, Andreas G.
AU - Apostolidis, Georgios K.
AU - Hadjileontiadis, Leontios J.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A well-known optimization-based method, the Variational Mode Decomposition (VMD), relies on the formulation of an optimization problem utilizing constant-bandwidth Wiener filters. However, this poses limitations in constant bandwidth and the need for constituent count. In this paper, the Dynamic Bandwidth VMD (DB-VMD) is proposed to generalize VMD by addressing the Wiener filter limitations through enhancement of the optimization problem with an additional constraint. Experiments in synthetic signals highlight DB-VMD's noise robustness and adaptability in comparison to VMD, paving the way for many applications, especially when the analyzed signals are contaminated with noise.
AB - Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches. A well-known optimization-based method, the Variational Mode Decomposition (VMD), relies on the formulation of an optimization problem utilizing constant-bandwidth Wiener filters. However, this poses limitations in constant bandwidth and the need for constituent count. In this paper, the Dynamic Bandwidth VMD (DB-VMD) is proposed to generalize VMD by addressing the Wiener filter limitations through enhancement of the optimization problem with an additional constraint. Experiments in synthetic signals highlight DB-VMD's noise robustness and adaptability in comparison to VMD, paving the way for many applications, especially when the analyzed signals are contaminated with noise.
KW - augmented Lagrangian
KW - Data-driven signal analysis
KW - dynamic bandwidth VMD
KW - non-stationary signal analysis
KW - variational mode decomposition (VMD)
UR - http://www.scopus.com/inward/record.url?scp=85195388976&partnerID=8YFLogxK
U2 - 10.1109/ICASSP48485.2024.10447179
DO - 10.1109/ICASSP48485.2024.10447179
M3 - Conference contribution
AN - SCOPUS:85195388976
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 9571
EP - 9575
BT - 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Y2 - 14 April 2024 through 19 April 2024
ER -