基于音频识别的自动门控制器设计

为解决草场车辆通行不便、传统围栏门效率低下等问题,在草场这种特殊环境下提出一种基于音频识别的自动门控制技术。该设计以STM32F407作为硬件核心,采用RT-Thread嵌入式实时操作系统。根据草场的环境复杂程度采集各种音频信号,采用快速傅里叶变换将音频信号变换至频域,主要针对车辆鸣笛声与其他干扰声音在频域的差别进行研究和分析,通过研究发现车辆的频谱具有多峰和倍频特性。根据这种特性设计音频识别算法,从而实现车辆识别。结果表明,相对于其他识别车辆方法,所提技术在成本和环保方面均更优,而且具有较高的识别精度和具有更加广阔的发展前景。

A Parametric Autoregressive Model for the Extraction of Electric Network Frequency Fluctuations in Audio Forensic Authentication

This paper proposes a new method for extracting ENF （electric network frequency） fluctuations from digital audio recordings for the purpose of forensic authentication. It is shown that the extraction of ENF components from audio recordings is realizable by applying a parametric approach based on an AR （autoregressive） model. The proposed method is compared to the existing STFT （short-time Fourier transform） based ENF extraction method. Experimental results from recorded electrical grid signals and recorded audio signals show that the proposed approach can improve the time resolution in the extracted ENF fluctuations and improve the detection of tampering with short alterations in longer audio recordings.