2026-09-19T00:54:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1502012023-04-01T03:23:09Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Chan, Vincent W.S.
Santarelli, Keith R.
Archer, William
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-03-31T14:39:15Z
2023-03-31T14:39:15Z
2023-02
2023-02-27T18:43:27.126Z
https://hdl.handle.net/1721.1/150201
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.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Visual Charting of Classified Audio Data
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="abee791b-aad7-4a1b-b0f3-d1d082917e5f">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Visual Charting of Classified Audio Data</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2023-02</PublicationDate>
<Authors>
<Author>
<DisplayName>Archer, William</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>https://rightsstatements.org/page/InC-EDU/1.0/</License>
<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>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>