Denoising by neural network for muzzle blast detection
This work addresses improved gunshot detection for military applications, but it is incremental as it applies a lightweight neural network to a known bottleneck in existing systems.
The paper tackles the problem of gunshot detection in noisy environments, particularly on moving military vehicles, by developing a lightweight neural network for denoising acoustic signals. The result is that the detection rate for muzzle blast waveforms is more than doubled when noise levels are comparable to the signal amplitude.
Acoem develops gunshot detection systems, consisting of a microphone array and software that detects and locates shooters on the battlefield. The performance of such systems is obviously affected by the acoustic environment in which they are operating: in particular, when mounted on a moving military vehicle, the presence of noise reduces the detection performance of the software. To limit the influence of the acoustic environment, a neural network has been developed. Instead of using a heavy convolutional neural network, a lightweight neural network architecture was chosen to limit the computational resources required to embed the algorithm on as many hardware platforms as possible. Thanks to the combination of a two hidden layer perceptron and appropriate signal processing techniques, the detection rate of impulsive muzzle blast waveforms (the wave coming from the detonation and indicating the position of the shooter) is significantly increased. With a rms value of noise of the same order as the muzzle blast peak amplitude, the detect rate is more than doubled with this denoising processing.