Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Six Noise Type Military Sound Classifier

Shelton, Christopher and Vipperman, Jeffrey (2013) Six Noise Type Military Sound Classifier. Master's Thesis, University of Pittsburgh. (Unpublished)

Primary Text

Download (1MB) | Preview


Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Vipperman, Jeffreyjsv@pitt.eduJSV
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorVipperman, Jeffreyjsv@pitt.eduJSV
Committee MemberCole, Danieldgcole@pitt.eduDGCOLE
Committee MemberClark, Williamwclark@pitt.eduWCLARK
Date: 24 September 2013
Date Type: Publication
Defense Date: 25 June 2013
Approval Date: 24 September 2013
Submission Date: 23 July 2013
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 154
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: ANN, Artificial Neural Network, Sound Classification
Date Deposited: 24 Sep 2013 20:32
Last Modified: 15 Nov 2016 14:14


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item