EVALUATION OF NONSTATIONARY VEHICLE PASSING LOUDNESS BASED ON AN ANTINOISE WAVELET PRE-PROCESSING NEURAL NETWORK MODEL

摘要

A new technique for sound loudness evaluation, the so-called antinoise wavelet preprocessing neural network (ANWT-NN) model, is presented in this paper. Based on passing vehicle noise, the ANWT-NN loudness model combines the techniques of wavelet analysis and neural network regression and classification. A wavelet-based, 21-point model for vehicle noise feature extraction is established. Verification shows that the trained ANWT-NN models are more accurate and effective than the WT-NN models for sound quality evaluation of nonstationary vehicle noises. The newly proposed ANWTNN model can be applied to both the stationary and nonstationary sound signals and even to the transient ones. The ANWT-NN technique is suggested not only for the prediction, classification, and comparison of the sound quality of passing vehicle noise, but also for applications in other sound-related engineering fields, in place of the conventional psychoacoustical models.