基于异常声音的隧道交通事故检测方法

针对公路隧道内交通事故的动态感知问题，在传统检测方法的基础上引入声学检测理论与方法，研究基于异常声音检测的隧道交通事故智能检测方法。通过分析短时能量（short term energy，STE）和梅尔倒谱系数（Mel-scale frequency cepstral coefficients，MFCC）检测方法在事故段特征表征以及精度干扰方面的缺陷，提出1种改进的融合特征MFCCE研究隧道环境下的交通事故检测。提取STE和MFCC特征并使用主成分分析（principal component analysis，PCA）进行特征融合得到新的融合特征MFCCE。以真实行车事故数据为基础，构建包含刹车与碰撞声的2段隧道噪声实验样本数据，分别对应早高峰时段（07:00—08:00）及平峰时段（12:00—13:00）的行车条件对隧道内的事故环境进行模拟分析，利用端点检测对所提方法进行验证并与其余2种方法进行对比分析。使用Pearson简单相关系数法作为最终的评价方法，通过该方法计算得到的相关系数r对比三种检测结果与原始样本的正相关相性。实验结果表明:STE在平峰及早高峰时段的相关系数分别为0.933和0.988；MFCC在平峰及早高峰时段的相关系数均为0.998；而无论在平峰还是早高峰时段，MFCCE的相关系数（0.999）均高于另外其他2种检测方法。MFCCE的平均相关系数相比于其他2种检测方法（STE、MFCC）分别提高了3.95%和1.00%。 

A Method for Detecting Traffic Accidents on Highway Tunnel Sections Based on Abnormal Sound

In response to the need of effectively detecting traffic accidents in highway tunnel sections, a novel acoustic detection method is introduced, so as to study an intelligent way for detecting traffic accidents in tunnels based on abnormal sound. By analyzing the issues of using Short-Term Energy (STE) and Mel-scale Frequency Cepstral Coefficients (MFCC) in identifying accident sections and interfering with precision, a modified fusion feature MFCCE is proposed to detect traffic accidents in tunnel sections. The new fusion feature of the MFCCE is obtained by extracting STE and MFCC features in virtue of Principal Component Analysis (PCA) to conduct feature fusion. Based on an observed traffic accident dataset, a sample dataset of noise experiments in two tunnels containing braking and collision sounds is developed, which corresponds to the traffic scenario of morning peak hours (from 07:00 to 08:00) and regular hours (from 12:00 to 13:00) respectively. Then an endpoint detection method is utilized to validate the proposed method, which is then compared with the other two methods (STE and MFCC). The Pearson correlation coefficient is determined as the final evaluation method, through which correlation coefficients r is used to compare the positive correlation of the three test results with the original samples. Experimental results show that the correlation coefficients of STE are 0.933 and 0.988 in the regular and morning peak hours respectively; the correlation coefficients of MFCC are 0.998 in both regular and morning peak hours, while the correlation coefficient of MFCCE (0.999) is higher than the other two detection methods in both regular and morning peak hours. The average correlation coefficients of MFCCE are 3.95% and 1.00% higher than the other two detection methods, respectively.