Detection and Localisation of Gunshots Using Sound Data
(2018) In Master's Theses in Mathematical Sciences FMAM05 20181Mathematics (Faculty of Engineering)
- Abstract
- The goal of this master's thesis is to detect and position sharp sounds using Axis speakers with built-in microphones. Sharp sounds of special interest are gunshots. The system needs at least five speakers to function and is designed for usage in indoor environments. The project follows a pipeline in order to position sound sources containing recording, synchronisation, detection of gunshot in sound data, and positioning of the sound. Detection of gunshots in recorded files is done by a binary classification with a deep neural network created in Python. The algorithms for positioning are implemented in MATLAB. The final neural network has an accuracy of 98%. It is pretrained by VGG-team with data from ILSVR and transfer learning is applied... (More)
- The goal of this master's thesis is to detect and position sharp sounds using Axis speakers with built-in microphones. Sharp sounds of special interest are gunshots. The system needs at least five speakers to function and is designed for usage in indoor environments. The project follows a pipeline in order to position sound sources containing recording, synchronisation, detection of gunshot in sound data, and positioning of the sound. Detection of gunshots in recorded files is done by a binary classification with a deep neural network created in Python. The algorithms for positioning are implemented in MATLAB. The final neural network has an accuracy of 98%. It is pretrained by VGG-team with data from ILSVR and transfer learning is applied to fit the model for gunshot data. After testing a few methods to synchronise the speakers and to calculate the position of the sound source, the final system has a mean error of 0.28 m. The model's precision is adequate for large areas. (Less)
- Popular Abstract
- In this master's thesis we study the possibilities to detect and position sharp sounds, for example gunshots, using only microphones. Our system requires minimal maintenance and is easy to use. The performance is adequate for large environments and results are achieved within seconds.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8953047
- author
- Chan, Martin LU and Karlsson, Sofie LU
- supervisor
- organization
- course
- FMAM05 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3350-2018
- ISSN
- 1404-6342
- other publication id
- 2018:E30
- language
- English
- id
- 8953047
- date added to LUP
- 2018-09-21 16:45:20
- date last changed
- 2018-10-11 16:19:11
@misc{8953047,
abstract = {{The goal of this master's thesis is to detect and position sharp sounds using Axis speakers with built-in microphones. Sharp sounds of special interest are gunshots. The system needs at least five speakers to function and is designed for usage in indoor environments. The project follows a pipeline in order to position sound sources containing recording, synchronisation, detection of gunshot in sound data, and positioning of the sound. Detection of gunshots in recorded files is done by a binary classification with a deep neural network created in Python. The algorithms for positioning are implemented in MATLAB. The final neural network has an accuracy of 98%. It is pretrained by VGG-team with data from ILSVR and transfer learning is applied to fit the model for gunshot data. After testing a few methods to synchronise the speakers and to calculate the position of the sound source, the final system has a mean error of 0.28 m. The model's precision is adequate for large areas.}},
author = {{Chan, Martin and Karlsson, Sofie}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master's Theses in Mathematical Sciences}},
title = {{Detection and Localisation of Gunshots Using Sound Data}},
year = {{2018}},
}