Ground Vehicle Acoustic Signal Processing Based on Biological Hearing Models

Abstract : This thesis presents a prototype vehicle acoustic signal classification system with low classification error and short processing delay. To analyze the spectrum of the vehicle acoustic signal, we adopt biologically motivated feature extraction models - cochlear filter and A1-cortical wavelet transform. The multi-resolution representation obtained from these two models is used in the later classification system. Different VQ based clustering algorithms are implemented and tested for real world vehicle acoustic signals. Among them, Learning VQ achieves the optimal Bayes classification performance, but its long search and training time make it not suitable for real time implementation. TSVQ needs a logarithmic search time and its tree structure naturally imitates the aggressive hearing in biological hearing systems, but it has a higher classification error. Finally, a high performance parallel TSVQ (PTSVQ) is introduced, which has classification performance close to the optimal LVQ, while maintains logarithmic search time. Experiments on ACIDS database show that both PTSVQ and LVQ achieve high classification rate. PTSVQ has additional advantages such as easy online training and insensitivity to initial conditions. All these features make PTSVQ the most promising candidate for practical system implementation.