Extracting single talker segments from audio mixtures in reverberant environments

Subramaniam, Avinash

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https://hdl.handle.net/2142/121540 Copy

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

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.

Graduation Semester

2023-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Avinash Subramaniam

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