{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121540"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121540","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Extracting single talker segments from audio mixtures in reverberant environments","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Avinash Subramaniam, accepted the attached license on 2023-07-17 at 18:13.","The student, Avinash Subramaniam, submitted this Thesis for approval on 2023-07-17 at 18:40.","This Thesis was approved for publication on 2023-07-18 at 11:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19722 on 2023-12-04 at 17:03:16","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Extracting single talker segments from audio mixtures in reverberant environments"]}]}],"canonical_facts":{"dc:contributor":["Choudhury, Romit Roy"],"dc:creator":["Subramaniam, Avinash"],"dc:date":["2023-08","2023-07-18"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Avinash Subramaniam, accepted the attached license on 2023-07-17 at 18:13.","The student, Avinash Subramaniam, submitted this Thesis for approval on 2023-07-17 at 18:40.","This Thesis was approved for publication on 2023-07-18 at 11:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19722 on 2023-12-04 at 17:03:16","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121540"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Avinash Subramaniam"],"dc:subject":["Binaural","Reverberant","Classification"],"dc:title":["Extracting single talker segments from audio mixtures in reverberant environments"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}