| dc.identifier.uri | http://hdl.handle.net/11401/77227 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Thesis | |
| dcterms.abstract | There have been numerous studies on the classification of auditory signals. In contrast,there have been very few studies in the implementation and classification of vehicles using purely auditory signals. This thesis presents an implementation of auditory vehicle identification using support vector machines. It explores how granular classification can be, from what type of vehicle to what action the vehicle is preforming. The granularity of the classification will greatly aid in auditory scene understanding. The classifications are done with computational complexity in mind, so embedded systems can utilize the findings. A simple averaging algorithm will also be explored that aids in classification significantly. | |
| dcterms.available | 2017-09-20T16:52:14Z | |
| dcterms.contributor | Doboli, Alex | en_US |
| dcterms.creator | Kaghaz-Garan, Scott Bejan | |
| dcterms.dateAccepted | 2017-09-20T16:52:14Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:14Z | |
| dcterms.description | Department of Computer Engineering. | en_US |
| dcterms.extent | 44 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77227 | |
| dcterms.issued | 2013-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:14Z (GMT). No. of bitstreams: 1
KaghazGaran_grad.sunysb_0771M_11682.pdf: 741793 bytes, checksum: 5ce31afc2544e4d357793977b0934a39 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Auditory Classification, Machine Learning, Scene understanding, Support Vector Machine, Vehicle sounds | |
| dcterms.subject | Computer engineering | |
| dcterms.title | Auditory Classification of Vehicles for Scene Understanding | |
| dcterms.type | Thesis | |
