Extracting single talker segments from audio mixtures in reverberant environments
Subramaniam, Avinash
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https://hdl.handle.net/2142/121540
Description
Title
Extracting single talker segments from audio mixtures in reverberant environments
Author(s)
Subramaniam, Avinash
Issue Date
2023-07-18
Director of Research (if dissertation) or Advisor (if thesis)
Choudhury, Romit Roy
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)
Binaural
Reverberant
Classification
Language
eng
Abstract
Classifying the number of simultaneous speakers in a reverberant environment remains a difficult problem to solve. The less complicated but no less important problem of detecting whether a single speaker or multiple speakers are present also poses a challenge, as multipath often distorts or alters the features commonly used in speaker number classification. When this classification system is used as a front-end to a learning-based system, then it becomes imperative that the classifier be accurate. This thesis presents SinguDetect, an end-to-end system which uses the inter-aural phase differences between a pair of in-ear microphones to classify each 64 ms time window of audio as belonging to one speaker or not. Even in heavily reverberant environments (RT60 = 2.0 s), SinguDetect is still able to maintain a precision of around 0.8 where the number of simultaneous sources is not higher than three, whereas other state-of-the-art algorithms tend to suffer decreases in precision and f-score in this range.
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