Firearm classification from acoustic signals using combined mel spectrogram, MFCC, LFCC, and CRNN networks

Discriminating the source of gunfire and accurately identifying the type of firearm used present a complex multifaceted challenge, requiring the integration of advanced acoustic signal processing techniques with cutting-edge deep learning methodologies. This research addresses the inherent difficulties in achieving reliable gunshot classification, which stem from the variability of acoustic signatures, environmental noise interference, and the need for real-time processing capabilities. The proposed system captures gunfire sounds using MEMS, processes the signals to extract discriminative features, and leverages deep learning models to classify firearm types. This work is conducted after addressing and emphasizing the challenges associated with acoustic principles, frequency modulation, acoustic pressure variations, and the acoustic fingerprints of different firearms. The system design phase highlights the technical complexities, the analysis of stringent requirements for real-world deployment, and the proposal and optimization of the model. The implementation involves collection of representative datasets, advanced data preprocessing techniques to mitigate noise and enhance signal quality, and the training of deep learning architectures using a novel feature set, termed MLFCC, which combines Mel spectrograms, MFCCs, and LFCCs. The proposed model demonstrates performance in real-time applications, effectively balancing accuracy and computational efficiency. Experimental results reveal the system's ability to handle the complexities of real-world scenarios while also exposing its limitations, particularly its sensitivity to extreme environmental conditions. This work underscores the significant technical challenges involved in developing gunfire discrimination system and highlights the critical role of interdisciplinary approaches in overcoming these obstacles. By pushing the boundaries of acoustic signal processing and deep learning, this research contributes to the advancement of public safety technologies, offering a foundation for innovations in this demanding field.