# Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller

## TL;DR

This paper introduces a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation, leveraging a multi-layer structure to better model nonlinear transformations and improve noise reduction performance.

## Contribution

The paper develops a novel DCMAC architecture with a modified backpropagation algorithm, extending traditional CMAC for enhanced nonlinear modeling in ANC applications.

## Key findings

- DCMAC outperforms conventional CMAC in residual noise reduction
- Deep structure enables better characterization of nonlinear transformations
- Experimental results demonstrate improved ANC performance

## Abstract

This paper proposes a deep cerebellar model articulation controller (DCMAC) for adaptive noise cancellation (ANC). We expand upon the conventional CMAC by stacking sin-gle-layer CMAC models into multiple layers to form a DCMAC model and derive a modified backpropagation training algorithm to learn the DCMAC parameters. Com-pared with conventional CMAC, the DCMAC can characterize nonlinear transformations more effectively because of its deep structure. Experimental results confirm that the pro-posed DCMAC model outperforms the CMAC in terms of residual noise in an ANC task, showing that DCMAC provides enhanced modeling capability based on channel characteristics.

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Source: https://tomesphere.com/paper/1705.00945