{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/95331"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/95331","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Example-based audio editing","abstract":"Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques.","abstract_html":"Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques.","abstract_has_math":false,"creators":["Anushiravani, Ramin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Smaragdis, Paris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-03-01T15:48:54Z","date_published":"2017-03-01T15:48:54Z","updated_at":"2026-07-22T22:26:37Z","subjects":["signal processing","speech enhancement","audio processing","audio editor","denoising","dereverberation","equalization","acoustic matching","digital audio work station","example-based editing","machine learning"],"languages":["en"],"rights":["Copyright 2016 Ramin Anushiravani"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/95331","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Smaragdis, Paris"]},{"key":"dc:creator","label":"Author","values":["Anushiravani, Ramin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-03-01T15:48:54Z","2016-11-21","2016-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["signal processing","speech enhancement","audio processing","audio editor","denoising","dereverberation","equalization","acoustic matching","digital audio work station","example-based editing","machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Ramin Anushiravani"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/95331"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Ramin Anushiravani, accepted the attached license on 2016-11-18 at 21:12.","The student, Ramin Anushiravani, submitted this Thesis for approval on 2016-11-18 at 21:19.","This Thesis was approved for publication on 2016-11-21 at 14:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10276 on 2017-02-28 at 14:49:51","Made available in DSpace on 2017-03-01T15:48:54Z (GMT). No. of bitstreams: 2 ANUSHIRAVANI-THESIS-2016.pdf: 11191981 bytes, checksum: 834f63428c2325d33f56e9d835b1a887 (MD5) LICENSE.txt: 4215 bytes, checksum: d4a387d9d37037d69b04fde4058fd35a (MD5) Previous issue date: 2016-11-21"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Example-based audio editing"]}]}],"canonical_facts":{"dc:contributor":["Smaragdis, Paris"],"dc:creator":["Anushiravani, Ramin"],"dc:date":["2017-03-01T15:48:54Z","2016-11-21","2016-12"],"dc:description":["Traditionally, audio recordings are edited through digital audio workstations (DAWs), which give users access to different tools and parameters through a graphical user interface (GUI) without prior knowledge in coding or signal processing. The complexity of working with DAWs and the undeniable need for strong listening skills have made audio editing unpopular among novice users and time consuming for professionals. We propose an intelligent audio editor (EBAE) that automates major audio editing routines with the use of an example sound and efficiently provides users with high-quality results. EBAE first extracts meaningful information from an example sound that already contains the desired effects and then applies them to a desired recording by employing signal processing and machine learning techniques.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-02-28 without embargo terms","The student, Ramin Anushiravani, accepted the attached license on 2016-11-18 at 21:12.","The student, Ramin Anushiravani, submitted this Thesis for approval on 2016-11-18 at 21:19.","This Thesis was approved for publication on 2016-11-21 at 14:11.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10276 on 2017-02-28 at 14:49:51","Made available in DSpace on 2017-03-01T15:48:54Z (GMT). No. of bitstreams: 2 ANUSHIRAVANI-THESIS-2016.pdf: 11191981 bytes, checksum: 834f63428c2325d33f56e9d835b1a887 (MD5) LICENSE.txt: 4215 bytes, checksum: d4a387d9d37037d69b04fde4058fd35a (MD5) Previous issue date: 2016-11-21"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/95331"],"dc:language":["en"],"dc:rights":["Copyright 2016 Ramin Anushiravani"],"dc:subject":["signal processing","speech enhancement","audio processing","audio editor","denoising","dereverberation","equalization","acoustic matching","digital audio work station","example-based editing","machine learning"],"dc:title":["Example-based audio editing"],"dc:type":["text"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:37Z"}