Morehead, AlexOgden, LaurenMagee, GabeHosler, RyanWhite, BruceMohler, George2020-12-112020-12-112019-12Morehead, A., Ogden, L., Magee, G., Hosler, R., White, B., & Mohler, G. (2019). Low Cost Gunshot Detection using Deep Learning on the Raspberry Pi. 2019 IEEE International Conference on Big Data (Big Data), 3038–3044. https://doi.org/10.1109/BigData47090.2019.9006456https://hdl.handle.net/1805/24601Many cities using gunshot detection technology depend on expensive systems that ultimately rely on humans differentiating between gunshots and non-gunshots, such as ShotSpotter. Thus, a scalable gunshot detection system that is low in cost and high in accuracy would be advantageous for a variety of cities across the globe, in that it would favorably promote the delegation of tasks typically worked by humans to machines. A repository of audio data was created from sound clips collected from online audio databases as well as from clips recorded using a USB microphone in residential areas and at a gun range. One-dimensional as well as two-dimensional convolutional neural networks were then trained on this sound data, and spectrograms created from this sound data, to recognize gunshots. These models were deployed to a Raspberry Pi 3 Model B+ with a short message service modem and a USB microphone attached, using a software pipeline to continuously analyze discrete two-second chunks of audio and alert a set of phone numbers if a gunshot is detected in that chunk. Testing found that a majority-rules ensemble of our one-dimensional and two-dimensional models fared best, with an accuracy above 99% on validation data as well as when distinguishing gunshots from fireworks. Besides increasing the safety standards for a city's residents, the findings generated by this research project expand the current state of knowledge regarding sound-based applications of convolutional neural networks.enPublisher Policymachine learningneural networksmicroprocessors and microcomputersArticle