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.
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