Research and development of sound quality in portable testing and evaluation system based on self-adaptive neural network

Abstract

Ordinary microphone cannot accurately reflect authentic human feelings in the sound quality testing and evaluation. By contrast, professional artificial head system can produce similar results with human feelings through the simulation of human auditory system, but it is expensive and unable to occupy the driver's seat to record the vehicle sound. Here we design a portable system based on self-adaptive neural network (NN) which can obtain the result of sound quality evaluation similar with the artificial head. The system consists of binaural microphone, signal conditioning units (SCU), and mobile phone. Firstly, sound is perceived by binaural microphone, processed by SCU, and collected by mobile phone. Initial sound quality objective parameters are calculated after noise reduction for sound signals and then the accurate objective parameters can be obtained from the initial parameters through the trained NN model. To train the model, the portable system and the HEAD system are simultaneously used to collect sound sample as the input and output sample sets under different conditions. After the input samples are preprocessed, wavelet entropy eigenvalues based on the best tree wavelet packet analysis are calculated along with psychoacoustic parameters of all the samples. Finally, we obtain the combined feature vectors and successfully train the model. Checked by the practical data, the relative error of sound quality objective parameters produced by the NN model is less than 5% compared with the HEAD system, which conforms to general engineering requirements. This inexpensive and user-friendly portable system paves a new way in the sound quality testing and evaluation that are highly desirable for vehicle acoustical designs and improvements.