Data-driven microphone array shape identification in reverberant environments
Sarkar, Kanad
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/122165
Description
Title
Data-driven microphone array shape identification in reverberant environments
Author(s)
Sarkar, Kanad
Issue Date
2023-12-08
Director of Research (if dissertation) or Advisor (if thesis)
Singer, Andrew C
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Semi-Supervised Learning
Spatial Audio
Array Processing
Deformable Arrays
Manifold Learning
Abstract
With microphone arrays, we can obtain spatial information with the recorded signals, which can be used to ascertain sound source location and allow for source separation. A common assumption with the application of spatial audio algorithms is that these microphone arrays remain in place during the recording. However, there are many scenarios where this is not the case. Prior work with deformable arrays suggests beamforming improvements when knowledge of the array shape is known. While, solutions for determining the array shape have been formulated in anechoic environments, these solutions are not feasible in a reverberant setting, as time delay estimates are subject to error. This thesis proposes a data-driven solution for array shape identification in reverberant environments, using the relative transfer function as a fingerprint for the array configuration. Not only does this thesis justify the relative transfer function as a fingerprint in this manner, but it also details a semi-supervised model to learn array deformation parameters.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.