Machine learning inspired efficient acoustic gunshot detection and localization system


  • Muhammad Salman Kabir
  • Dr Junid Mir University of Engineering and Technology Taxila
  • Caleb Rascon
  • Muhammad Laiq Ur Rahman Shahid
  • Furqan Shaukat


Event Detection, Gunshot Detection, Audio Features, Audio Localization


Gunshot detection and localization is a frontier technology in security systems. With an increasing rate of shootings globally, gunshot events and directional awareness are crucial for the law enforcement agencies for a timely response. This paper presents a real-time computational efficient gunshot detection and localization system. First, the performance of Mel-frequency cepstral coefficients, linear prediction coefficients, Gammatone cepstral coefficients, and spectral centroid as an audio feature for acoustic gunshot detection is thoroughly analyzed. Then, a bagged tree ensemble and support vector machine classifiers are trained and tested on a diverse gunshot database under different SNR settings, using a 10-fold validation technique. The detection accuracy of 97.3% with a sensitivity of 0.978 and a specificity of 0.988 is achieved. The test-train curves corroborate the fitness and generalization of the trained detection model. After the detection, the localization is performed by calculating the arrival time difference using a general cross-correlation phase transform. Finally, the system is implemented on an experimental test-bed for real-time performance evaluation. Field tests indicate the proposed system's effectiveness to detect and localize a gunshot in 0.7-1 seconds.




How to Cite

Kabir, M. . S., Dr Junid Mir, Rascon, C., Shahid, M. L. U. R., & Shaukat, F. (2022). Machine learning inspired efficient acoustic gunshot detection and localization system. University of Wah Journal of Computer Science, 3(1). Retrieved from