{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150201"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150201","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Visual Charting of Classified Audio Data","abstract":"To analyze hundreds of hours of audio recorded in field testing, it is useful to use previously trained machine learning algorithms that can classify the data with discrete time-stamped events. However, this is only a first step. A programmatic method is needed to make sense of the thousands of classified events. In a field testing environment, new charts need to be generated quickly so that conclusions can be drawn while the field test is still ongoing. The generation of these charts also needs to be flexible enough to quickly respond to any on-the-fly changes. This thesis describes the development of a highly customizable method to visually chart labeled audio data in an easily understandable format.","abstract_html":"To analyze hundreds of hours of audio recorded in field testing, it is useful to use previously trained machine learning algorithms that can classify the data with discrete time-stamped events. However, this is only a first step. A programmatic method is needed to make sense of the thousands of classified events. In a field testing environment, new charts need to be generated quickly so that conclusions can be drawn while the field test is still ongoing. The generation of these charts also needs to be flexible enough to quickly respond to any on-the-fly changes. This thesis describes the development of a highly customizable method to visually chart labeled audio data in an easily understandable format.","abstract_has_math":false,"creators":["Archer, William"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Chan, Vincent W.S.","Santarelli, Keith R."],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02","date_published":"2023-02","updated_at":"2026-07-22T22:21:57Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/150201","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chan, Vincent W.S.","Santarelli, Keith R."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Archer, William"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-03-31T14:39:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-31T14:39:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/150201"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["To analyze hundreds of hours of audio recorded in field testing, it is useful to use previously trained machine learning algorithms that can classify the data with discrete time-stamped events. However, this is only a first step. A programmatic method is needed to make sense of the thousands of classified events. In a field testing environment, new charts need to be generated quickly so that conclusions can be drawn while the field test is still ongoing. The generation of these charts also needs to be flexible enough to quickly respond to any on-the-fly changes. This thesis describes the development of a highly customizable method to visually chart labeled audio data in an easily understandable format."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Visual Charting of Classified Audio Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chan, Vincent W.S.","Santarelli, Keith R."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Archer, William"],"dc:date.accessioned":["2023-03-31T14:39:15Z"],"dc:date.available":["2023-03-31T14:39:15Z"],"dc:date.issued":["2023-02"],"dc:description.abstract":["To analyze hundreds of hours of audio recorded in field testing, it is useful to use previously trained machine learning algorithms that can classify the data with discrete time-stamped events. However, this is only a first step. A programmatic method is needed to make sense of the thousands of classified events. In a field testing environment, new charts need to be generated quickly so that conclusions can be drawn while the field test is still ongoing. The generation of these charts also needs to be flexible enough to quickly respond to any on-the-fly changes. This thesis describes the development of a highly customizable method to visually chart labeled audio data in an easily understandable format."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/150201"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Visual Charting of Classified Audio Data"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:57Z"}