Incremental Learning Based Adaptive Filter for Nonlinear Distributed Active Noise Control System

• Main Authors: Ruchi Kukde, M. Sabarimalai Manikandan, Ganapati Panda
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Open Journal of Signal Processing
• Subjects: Acoustic sensor nodes; active noise control (ANC); adaptive signal processing; distributed processing; functional link artificial neural networks; incremental strategy
• Online Access: https://ieeexplore.ieee.org/document/9006935/

Active control of noise for multi-channel applications is affected by the existence of nonlinear primary and secondary paths. There is a degradation in the performance of linear multi-channel active noise control (LMANC) systems based on minimization of sum of squared errors obtained from multiple sensors in presence of nonlinear primary path (NPP) and nonlinear secondary path (NSP) conditions. The NPP and NSP problems are more prominent and challenging for multi-point noise control applications owing to different locations of silent zones and acoustic coupling between secondary sources and error sensors. In order to surmount this problem, an incremental strategy based nonlinear distributed ANC (NDANC) system is developed in this article. The adaptive exponential functional link network (AE-FLN) is employed as an adaptive control unit at the acoustic sensor nodes (ASNs) for the design of NDANC system. The incremental co-operation scheme is utilized to provide uniform noise cancellation in presence of NPP and NSP conditions. Simulation study is conducted extensively to demonstrate the efficiency of the proposed system for different practical NPP and NSP scenarios. The detailed computational load analysis and subjective evaluation of reduction in perceptual noise levels are performed for different real noise conditions.