System Modeling for Active Noise Control with Reservoir Computing

Abstract

Abstract-This paper investigates the use of reservoir computing for active noise control (ANC). It is shown that the ANC problem can be solved by a concatenation of physically present subsystems. These subsystems can be modelled by reservoirs that are trained, using one shot learning. This approach is compared to genetic algorithms tuning a Volterra filter. Experimental results show that our approach works well as system model, meaning that a reservoir trained on white noise performs good on other input signals as well. This is a major advantage over genetic algorithms that generalize rather badly. Furthermore, our approach needs less data and this data can be gathered in one experiment only.