Data-driven microphone array shape identification in reverberant environments
Sarkar, Kanad
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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
Language
en
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.
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