Acoustic Signal based Traffic Density State Estimation using Adaptive Neuro-Fuzzy Classifier

摘要

Traffic monitoring and parameters estimation from urban to battlefield environment traffic is fast-emerging field based on acoustic signals. This paper considers the problem of vehicular traffic density state estimation, based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). The (Scaled Conjugate Gradient) SCG algorithm, which is a supervised learning algorithm for network-based methods, is used to computes the second-order information from the two first-order gradients of the parameters by using all the training datasets. Adaptive Neuro-Fuzzy classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. Adaptive Neuro-Fuzzy classifier is used to classify the acoustic signal segments spanning duration of 20-40 s, which results in a classification accuracy of 93.33% for 13-D MFCC coefficients and around 96% when entire features were considered, 77.78% for first order derivatives and ～75% for second order derivatives of cepstral coefficients.