Properties of FXLMS-based narrowband active noise control with online secondary-path modeling

Authors:
Yegui Xiao;Liying Ma;Koji Hasegawa
Affiliations:
Department of Management and Information Systems, Prefectural University of Hiroshima, Hiroshima, Japan;Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan;Department of Management and Information Systems, Hiroshima Prefectural Technology Research Institute, Hiroshima, Japan
Venue:
IEEE Transactions on Signal Processing
Year:
2009

Citing 6
Cited 1

Adaptive signal processing

Adaptive signal processing
Active Noise Control Systems: Algorithms and DSP Implementations

Active Noise Control Systems: Algorithms and DSP Implementations
Stochastic Analysis of the FXLMS-Based Narrowband Active Noise Control System

IEEE Transactions on Audio, Speech, and Language Processing
Analysis of the filtered-X LMS algorithm and a related new algorithm for active control of multitonal noise

IEEE Transactions on Audio, Speech, and Language Processing
A new variable step size LMS algorithm-based method for improved online secondary path modeling in active noise control systems

IEEE Transactions on Audio, Speech, and Language Processing
A New Robust Narrowband Active Noise Control System in the Presence of Frequency Mismatch

IEEE Transactions on Audio, Speech, and Language Processing

Analysis of online secondary-path modeling with auxiliary noise scaled by residual noise signal

IEEE Transactions on Audio, Speech, and Language Processing

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Abstract

Rotating machines such as diesel engines, cutting machines, fans, motors, etc., generate sinusoidal noise signals that may be effectively reduced by narrowband active noise control (ANC) systems. In this paper, a typical filtered-X LMS (FXLMS) based narrowband ANC system equipped with an online secondary-path modeling subsystem is analyzed in detail. First, difference equations governing the dynamics of the FXLMS algorithm for secondary source synthesis and the LMS algorithm for secondary-path estimation are derived in terms of convergence in both mean and mean square. Steady-state expressions for mean-square error (MSE) as wetl as the residual noise power are then developed in closed form. Extensive simulations are performed to demonstrate the validity of the analytical results.