Optimal Output-Constrained Active Noise Control Based on Inverse Adaptive Modeling Leak Factor Estimate

• Published in: IEEE/ACM transactions on audio, speech, and language processing Vol. 29; pp. 1256 - 1269
• Main Authors: Shi, Dongyuan, Gan, Woon-Seng, Lam, Bhan, Wen, Shulin, Shen, Xiaoyi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; Active noise control; Adaptation models; Adaptive control; Algorithms; audio output saturation; Constraints; Control stability; leaky FxLMS algorithm; Mean square error methods; Microphones; Modelling; Noise control; Nonlinear distortion; Optimal control; optimal leak factor; output constrain; Power amplifiers; Process control; quadratically constrained quadratic program; Saturation; Speech processing
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2021.3065730

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.