Traffic density state estimation based on acoustic fusion

In this paper, we propose an acoustic fusion based approach to classify the traffic density states. In particular, we combine the information from mel-frequency cepstral coefficients (MFCC) based classifier, which models the cumulative road side signal and honk event based classifier. Honk based classifier is obtained by modeling the honk statistics for each traffic class, viz., Jam, Medium and Free. We study in detail the discriminative capabilities of honk information based classifier. Decisions from MFCC and honk classifier are then combined in probabilistic framework with an appropriate fusion strategy. We also propose to use prior honk information in-order to further improve the classification results. Classification results show good performance even with 10s of audio data.