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A simultaneous perturbation stochastic approximation algorithm for broadband noise control.

Shanjun Li1, Guoyong Jin1, Muyun Wu2

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|February 2, 2023
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This study introduces a new Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm for active noise control of broadband noise, even with background interference. The enhanced algorithm shows improved performance and convergence over existing methods.

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Area of Science:

  • Signal Processing
  • Control Systems Engineering
  • Acoustics

Background:

  • Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms are used in active noise control.
  • Existing SPSA methods primarily address periodic noise and do not effectively handle broadband noise or background interference.
  • There is a need for improved SPSA algorithms capable of broadband noise control in the presence of noise.

Purpose of the Study:

  • To propose a novel SPSA-based algorithm for active control of broadband noise.
  • To incorporate a specific cost function to enhance the algorithm's performance in noisy environments.
  • To analyze and validate the proposed algorithm's effectiveness and convergence properties.

Main Methods:

  • Developed a new algorithm combining a cost function with the SPSA algorithm.
  • The cost function utilizes an inner product of the cross-correlation between a reference vector (past reference signals) and the error signal.
  • Algorithm analysis and numerical simulations were conducted to verify its performance.

Main Results:

  • The proposed algorithm effectively reduces broadband noise, even when interference noise is present.
  • Numerical simulations confirmed the algorithm's validity and superior performance.
  • The new algorithm demonstrated better convergence compared to existing SPSA algorithms.

Conclusions:

  • The proposed SPSA algorithm with the novel cost function is effective for broadband active noise control.
  • The algorithm shows enhanced performance and convergence, particularly in the presence of background noise.
  • This work provides a valuable advancement for active noise control applications dealing with complex noise environments.