Scaling IoT-based noise cancellation to multiple noise sources

A recent development integrating Internet-of-Things (IoT) sensing techniques with active noise cancellation (ANC) has demonstrated certain benefits over the conventional methods for ANC, including wideband cancellation without blocking the ear and non-causal adaptive filtering. These benefits, however, can only be observed in acoustic environments with a single noise source. This thesis presents a new design for an IoT-based active noise cancellation system that can effectively cancel multiple independent noise sources. By incorporating multiple reference microphone inputs, the new system can estimate the unique acoustic channels between different sources of noise and the listener. Through simulation and hardware experiments, this new design is evaluated and shown to achieve significant improvement in cancellation over the previous implementation of IoT-based ANC.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Collins, Michael Liam

Contributors dc:contributor

• Hassanieh, Haitham

• Active noise cancellation

Statement dc:rights

• Copyright 2020 Michael Collins

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108186

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108186