用轻量神经网络提升枪声探测在嘈杂环境下的准确性
Denoising by neural network for muzzle blast detection
- 采用两层感知机结合信号处理去噪
- 噪声与枪声峰值相当条件下，检测率翻倍
- 适合嵌入车载等资源受限平台
Acoem 开发了基于麦克风阵列与软件的枪声探测系统，用于战场定位开火者。系统性能受声学环境影响显著，尤其在移动军用车辆上，噪声会降低探测效果。为此，开发了一种轻量级神经网络以降低计算资源需求，便于部署于多种硬件平台。该方法结合两层感知机与适当的信号处理技术，显著提升了对瞬态枪口爆震波形（指示射手位置）的检测能力。当噪声均方根值与枪口爆震峰值幅度同阶时，经去噪处理后检测率超过两倍。
原文摘要 · Abstract (English)
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.
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