Improved Gunshot Classification by Using Artificial Data

Busse C, Krause T, Ostermann J, Bitzer J. Improved Gunshot Classification by Using Artificial Data. In: AES Conference: 2019 AES International Conference on Audio Forensics. Audio Engineering Society; 2019. Paper 18. Available from: https://aes.org/publications/elibrary-page/?id=20469

@inproceedings{Busse2019_20469,
  author = {Busse, Christian and Krause, Thomas and Ostermann, Jörn and Bitzer, Jörg},
  title = {{Improved Gunshot Classification by Using Artificial Data}},
  booktitle = {AES Conference: 2019 AES International Conference on Audio Forensics},
  note = {Paper 18},
  year = {2019},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20469}
}

TY  - CPAPER
TI  - Improved Gunshot Classification by Using Artificial Data
AU  - Busse, Christian
AU  - Krause, Thomas
AU  - Ostermann, Jörn
AU  - Bitzer, Jörg
T2  - AES Conference: 2019 AES International Conference on Audio Forensics
M1  - Paper 18
PY  - 2019
DA  - 2019/06/06
UR  - https://aes.org/publications/elibrary-page/?id=20469
PB  - Audio Engineering Society
LA  - en
AB  - Gunshot classi?cation in audio ?les is used in forensics, surveillance, and multimedia analysis. In this contribution we show that it is possible to use data augmentation in order to enlarge the training set of a rare event like a gunshot with arti?cial data based on a simple but suf?cient model, and a database of room impulse responses. The results indicate that the enlarged database increases the accuracy in a classi?cation task signi?cantly, even if no real data is used for training at all.
ER  -