An Efficient & Less Complex Solution to Mitigate Impulsive Noise in Multi-Channel Feed-Forward ANC System with Online Secondary Path Modeling (OSPM)

This paper deals with impulsive noise (IN) in multichannel (MC) Active Noise Control (ANC) Systems with Online Secondary Path Modelling (OSPM) employing adaptive algorithms for the first time. It compares performance of various existing techniques belonging to varied computational complexity range and proposes four new methods, namely: FxRLS-VSSLMS, VSSLMS-VSSLMS, FxLMAT-VSSLMS and NSS MFxLMAT-VSSLMS to deal with modest to very high impulsive noise (IN). Simulation results show that these proposed methods demonstrated improved performance in terms of fast convergence speed, lowest steady state error, robustness and stability under impulsive environment in addition to modelling accuracy for stationary as well as non-stationary environment besides reducing computational complexity many folds

Adaptive frequency domain identification for ANC systems using non-stationary signals

The problem of identification of secondary path in active noise control applications is dealt with fundamentally using time-domain adaptive filters. The use of adaptive frequency domain subband identification as an alternative has some significant advantages which are overlooked in such applications. In this paper two different delayless subband adaptive algorithms for identification of an unknown secondary path in an ANC framework are utilized and compared. Despite of reduced computational complexity and increase convergence rate this approach allows us to use non-stationary audio signals as the excitation input to avoid injection of annoying white noise. For this purpose two non-stationary music and speech signals are used for identification. The performances of the algorithms are measured in terms of minimum mean square error and convergence speed. The results are also compared to a fullband algorithm for the same scenario. The proposed delayless algorithms have a closed loop structure with DFT filterbanks as the analysis filter. To eliminate the delay in the signal path two different weights transformation schemes are compared

Decorrelation control by the cerebellum achieves oculomotor plant compensation in simulated vestibulo-ocular reflex

We introduce decorrelation control as a candidate algorithm for the cerebellar microcircuit and demonstrate its utility for oculomotor plant compensation in a linear model of the vestibulo-ocular reflex (VOR). Using an adaptive-filter representation of cerebellar cortex and an anti-Hebbian learning rule, the algorithm learnt to compensate for the oculomotor plant by minimizing correlations between a predictor variable (eye-movement command) and a target variable (retinal slip), without requiring a motor-error signal. Because it also provides an estimate of the unpredicted component of the target variable, decorrelation control can simplify both motor coordination and sensory acquisition. It thus unifies motor and sensory cerebellar functions

A New Variable Regularized Transform Domain NLMS Adaptive Filtering Algorithm-Acoustic Applications and Performance Analysis

published_or_final_versio

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation). We also present two detailed extensions of Bayesian optimization, with experiments---active user modelling with preferences, and hierarchical reinforcement learning---and a discussion of the pros and cons of Bayesian optimization based on our experiences

Computationally efficient adaptive algorithms for active control systems

University of Technology Sydney. Faculty of Engineering and Information Technology.As the side effect of urbanization, acoustic noise from various sources is the most common health threat in our day-to-day life. Passive noise control methods are constrained by several factors such as frequency content of noise, absorbing material type, thickness and geometry. Alternatively, active noise control method has emerged as a promising solution to control low-frequency noise cost-effectively. In an active noise control system, the acoustic path from the control source to the error microphone affects the control performance. If the reference microphone is placed in close proximity of the control source, an unwanted acoustic feedback signal from the control source will be captured by the reference microphone, which may lead to system instability. Furthermore, the adaptive control algorithms have a high computational complexity, which limits its application with high sampling frequency and the scalability of a control system for generating a larger quiet zone. Various algorithms have been proposed in literature for modelling acoustic paths, low-complexity implementation of single and multiple channel control systems. However, they are still constrained by factors such as computational complexity, noise reduction performance, causality issue and stability issue. The objectives of this PhD research are to develop low-complexity algorithms for (1) online modelling of acoustic paths without affecting noise reduction performance, (2) achieving improved control performance at transient and steady state, (3) high sampling frequency operation and broadband noise control and (4) multiple channel decentralized algorithm for broadband noise control. […] In summary, online acoustic path modelling methods are proposed using the control signal; an affine combination of adaptive filters are proposed for improved control performance; a time-frequency domain flexible structure is proposed for active control operation for high sampling frequency operation; a decentralized algorithm is proposed to achieve similar noise reduction performance as the centralized one for broadband control