A Recurrent Neural Filter for Adaptive Noise Cancellation

Abstract

This paper presents a dynamic neural filter for adaptive noise cancellation. The cancellation task is transformed to a system-identification problem, which is tackled by use of the Block-Diagonal Recurrent Neural Network. The filter is applied to a benchmark noise cancellation problem, where a comparative analysis with a series of other dynamic models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.