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HRABINA, M.; SIGMUND, M.
Original Title
Gunshot Recognition using Low Level Features in the Time Domain
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
This paper explores the possibility of using scarcely used time-domain features for the task of gunshot recognition. A set of 11 features derived from temporal characteristics (waveform) of signals is calculated from a mixed dataset of gunshots and non-gunshots. The features leverage the impulsive nature of gunshots and their dissimilarity to other, especially more stationary signals. The paper includes a description of feature extraction, distribution of features and their recognition performance on a selected audio dataset. A subset achieves promising results in comparison with more frequently used spectral-domain features. This makes them a valuable addition to other frequently used features, especially for tasks of impulsive sound recognition.
English abstract
Keywords
gunshot detection, feature extraction, time-domain features, audio processing
Key words in English
Authors
RIV year
2019
Released
19.04.2018
Publisher
IEEE
ISBN
978-1-5386-2485-2
Book
Proceedings of 28th International Conference Radioelektronika 2018
Pages from
1
Pages to
5
Pages count
URL
https://ieeexplore.ieee.org/document/8376372/
Full text in the Digital Library
http://hdl.handle.net/11012/83819
BibTex
@inproceedings{BUT147072, author="Martin {Hrabina} and Milan {Sigmund}", title="Gunshot Recognition using Low Level Features in the Time Domain", booktitle="Proceedings of 28th International Conference Radioelektronika 2018", year="2018", pages="1--5", publisher="IEEE", doi="10.1109/RADIOELEK.2018.8376372", isbn="978-1-5386-2485-2", url="https://ieeexplore.ieee.org/document/8376372/" }
Documents
PID5269693gunshot-recognition-vut