{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84083"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84083","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Acquisition and Classification of Automobile Audio Signals for Dataset Generation","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Donnelly, Margaret; 0000-0002-2165-0630"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Zirnheld, Jennifer","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:45Z","date_published":"2022-06-21T15:47:45Z","updated_at":"2026-07-27T19:05:30Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84083","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zirnheld, Jennifer","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Donnelly, Margaret; 0000-0002-2165-0630"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:45Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84083"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","Classification is an integral part of categorizing scientific data in order to assist human understanding of the context in which a signal is generated. This work seeks to classify an audio sample recorded from an automobile engine in order to diagnose the health of a vehicle. To establish this process, studies were performed by monitoring the resultant recordings after manipulating the configuration settings of the external microphone as well as by performing an analysis on the frequency spectrum of a segment of the audio samples. Diagnosis via these characteristics reduces discrepancies introduced by the subjective nature of audible interpretation due to the limitations of the human auditory field. Additionally, a more precise understanding of engine characteristics leads to cleaner dataset generation and granularity in engine health categorization.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Acquisition and Classification of Automobile Audio Signals for Dataset Generation"]}]}],"canonical_facts":{"dc:contributor":["Zirnheld, Jennifer","Electrical Engineering"],"dc:creator":["Donnelly, Margaret; 0000-0002-2165-0630"],"dc:date":["2022-06-21T15:47:45Z","2020"],"dc:description":["M.S.","Classification is an integral part of categorizing scientific data in order to assist human understanding of the context in which a signal is generated. This work seeks to classify an audio sample recorded from an automobile engine in order to diagnose the health of a vehicle. To establish this process, studies were performed by monitoring the resultant recordings after manipulating the configuration settings of the external microphone as well as by performing an analysis on the frequency spectrum of a segment of the audio samples. Diagnosis via these characteristics reduces discrepancies introduced by the subjective nature of audible interpretation due to the limitations of the human auditory field. Additionally, a more precise understanding of engine characteristics leads to cleaner dataset generation and granularity in engine health categorization.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84083"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["Acquisition and Classification of Automobile Audio Signals for Dataset Generation"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:30Z"}