Fast Adaptive Active Noise Control Based on Modified Model-Agnostic Meta-Learning Algorithm

With the advent of efficient low-cost processors and electroacoustic components, there is renewed interest in the practical implementation of active noise control (ANC). However, the slow convergence of conventional adaptive algorithms deployed in ANC restricts its handling of typical amplitude-vary...

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Veröffentlicht in:	IEEE signal processing letters Jg. 28; S. 593 - 597
Hauptverfasser:	Shi, Dongyuan, Gan, Woon-Seng, Lam, Bhan, Ooi, Kenneth
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Acceleration Active noise control Adaptation models Adaptive algorithms Adaptive control Adaptive filters Adaptive systems Amplitudes control filter initialization Convergence Cost function fast adaptation FxLMS LF noise Machine learning Mathematical models model-agnostic meta-learning Noise control Noise measurement Signal processing algorithms
ISSN:	1070-9908, 1558-2361
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Abstract	With the advent of efficient low-cost processors and electroacoustic components, there is renewed interest in the practical implementation of active noise control (ANC). However, the slow convergence of conventional adaptive algorithms deployed in ANC restricts its handling of typical amplitude-varying noise. Hence, we proposed a modified model-agnostic, meta-learning (MAML) strategy to obtain an initial control filter, which accelerates an adaptive algorithm's convergence when dealing with different types of amplitude-varying low-frequency noise. Numerical simulations with measured paths and real noise sources demonstrate its convergence acceleration efficacy in practical scenarios.
AbstractList	With the advent of efficient low-cost processors and electroacoustic components, there is renewed interest in the practical implementation of active noise control (ANC). However, the slow convergence of conventional adaptive algorithms deployed in ANC restricts its handling of typical amplitude-varying noise. Hence, we proposed a modified model-agnostic, meta-learning (MAML) strategy to obtain an initial control filter, which accelerates an adaptive algorithm's convergence when dealing with different types of amplitude-varying low-frequency noise. Numerical simulations with measured paths and real noise sources demonstrate its convergence acceleration efficacy in practical scenarios.
Author	Ooi, Kenneth Shi, Dongyuan Gan, Woon-Seng Lam, Bhan
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SubjectTerms	Acceleration Active noise control Adaptation models Adaptive algorithms Adaptive control Adaptive filters Adaptive systems Amplitudes control filter initialization Convergence Cost function fast adaptation FxLMS LF noise Machine learning Mathematical models model-agnostic meta-learning Noise control Noise measurement Signal processing algorithms
Title	Fast Adaptive Active Noise Control Based on Modified Model-Agnostic Meta-Learning Algorithm
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