Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


36826.pdf (6.89 MB)

ETD Abstract Container

Abstract Header

Subband Adaptive Filtering for Active Broadband Noise Control with Application to Road Noise inside Vehicles

Long, Guo
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846098921553

Abstract Details

2020, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Broadband active noise control (ANC) presents challenges due to both computational demand and slow convergence. Subband adaptive filter (SAF) has hence been introduced to address these challenges, especially when the broadband noise is with large spectral dynamic range and the ANC system model has long impulse response. The study of subband ANC usually requires a broad understanding of the general broadband ANC, as well as the SAF architecture including the analysis filter bank for signal decomposition, the adaptive algorithm for subband filter weights adaptation, and the weight stacking for combining subband filter weights into a fullband noise cancelling filter. A more in-depth understanding of a general broadband ANC is needed to help predict the potentially maximum attenuation level of a subband ANC, and guide the way how the system could be effectively initialized for rapid convergence. The design of SAF architecture for broadband ANC remains very challenging, which needs a trade-off between various effects including delay and spectral leakage due to analysis filter bank, computational cost of subband adaptive algorithm, and weight stacking distortion. This necessitates an effective method to find out an optimal parametric design solution for the subband ANC. In practice, many unwanted interferences and high-amplitude disturbances may threaten the effectiveness, stability and robustness of subband ANC, which has not been thoroughly investigated. Thus, we develop an effective and computationally efficient subband ANC system with robust adaptive control algorithms for broadband noise. First, this dissertation presents the maximum achievable noise reduction of a general feedforward broadband ANC system corresponding to the optimal filter under causality constraint. This optimal causal filter helps guide the initialization of the filter with finite length, and guarantees rapid convergence and maximum attenuation performance. Besides theoretical analysis, numerical simulations assess the detrimental effect of uncorrelated interference and the limitation of current coherence-based maximum attenuation prediction. Next, by incorporating genetic algorithm (GA) optimization into the parametric design of subband ANC, an efficient way to determine an optimal set of configurable parameters has been developed for a wideband attenuation in the frequency range of interest. Numerical simulations show the efficacy of applying the GA optimization for automated tuning of subband ANC, particularly for multi-channel subband ANC. Then, a novel robust and computationally efficient subband ANC algorithm is proposed for broadband noise with or without impulsive interference. By introducing bounded variable step sizes and a robust criteria, fast convergence is achieved and signal outliers are effectively suppressed. This avoids prohibitively heavy computational burden owing to threshold on the reference and/or error signal as applied in the state-of-the-art robust ANC algorithms. Numerical simulations validate the enhanced performance of the proposed algorithm for various synthesized broadband noises with or without impulsive interferences. Finally, a concluding work is conducted to apply the proposed subband algorithm for actual road noise inside vehicles in the simulation environment. With an effective initialization and optimal parametric design, the superior performance of the proposed subband ANC algorithm is confirmed in a multi-channel road noise cancellation (RNC) system where 3.18 dB attenuation on average over 50 Hz ~ 500 Hz is achieved.
Jay Kim, Ph.D. (Committee Chair)
Manish Kumar, Ph.D. (Committee Member)
Teik Lim, Ph.D. (Committee Member)
Yongfeng Xu, Ph.D. (Committee Member)
174 p.
Mechanical Engineering
Active noise control; Broadband noise; Subband adaptive filter; Road noise; Robust algorithm; Genetic algorithm

Recommended Citations

Citations

  • Long, G. (2020). Subband Adaptive Filtering for Active Broadband Noise Control with Application to Road Noise inside Vehicles [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846098921553

    APA Style (7th edition)

  • Long, Guo. Subband Adaptive Filtering for Active Broadband Noise Control with Application to Road Noise inside Vehicles. 2020. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846098921553.

    MLA Style (8th edition)

  • Long, Guo. "Subband Adaptive Filtering for Active Broadband Noise Control with Application to Road Noise inside Vehicles." Doctoral dissertation, University of Cincinnati, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595846098921553

    Chicago Manual of Style (17th edition)

ucin1595846098921553
635
© 2020, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.