In this work, a pre-trained convolutional neural network is employed to detect gunfire from audio excerpts of urban sounds. The pretrained convolutional neural network is fined-tuned with transfer learned features to a new task using a smaller number of training signals. Two CNN methods are applied to the time-frequency representation of the audio signals. The first CNN method is based on classifying specific events in audio signals. The second CNN method is an image-based analysis method. The accuracy of the two CNN results will be compared and analyzed based on gunfire type and retrieved urban multipath conditions. A k-means clustering algorithm is employed to identify gunfire types and parametric modeling to retrieve the urban multipath conditions.
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September 12 2023
Gunshot detection from audio excerpts of urban sounds using transfer learning
John Irungu, Jamelia Ancel, Wagdy Mahmoud, Max Denis; Gunshot detection from audio excerpts of urban sounds using transfer learning. Proc. Mtgs. Acoust. 8 May 2023; 51 (1): 045003. https://doi.org/10.1121/2.0001783
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