Vehicle interior sound quality evaluation using energy based features

Abstract

The comfort in the car interior is already become a need for the passengers and the buyers. Due to high competition in car industries, all the car manufacturers are concentrating in improving the interior noise comfort of the car. Vehicle Noise Comfort Index (VNCI) has been developed recently to evaluate the sound characteristics of passenger cars. VNCI indicates the interior vehicle noise comfort using a numeric scale from 1 to 10. Most of the researches are relating the vehicle interior sound quality to psychoacoustics sound metrics such as loudness and sharpness for the frequency between 20 Hz to 20 kHz. In this present paper, a vehicle comfort level indication is proposed to detect the comfort level in cars using artificial neural network. Determination of vehicle comfort is important because continuous exposure to the noise and vibration leads to health problems for the driver and passengers. The database of sound samples from 15 local cars is used. The sound samples are taken from two states, while the car is in stationary condition and while it is moving at a constant speed. The energy level is extracted from the signals. The correlation between the subjective and the objective evaluation is also tested. The relationship between the VNCI and the energy level is modelled using a feed-forward neural network trained by back-propagation algorithm.