Active sound quality control of vehicle interior noise using a dual sampling-rate active noise equalization algorithm

Abstract

In-vehicle active sound quality control (ASQC) system is an advanced application of active noise control (ANC) technology, which always suppresses the interior sound pressure or improves the psychoacoustic features. However, the connection between the sound quality control state and the vehicle's working state is normally ignored, and the passenger's sound perception typically expects a linear relationship between the vehicle speed and sound loudness. This paper presents a dual sampling-rate active noise equalization algorithm. The low sampling-rate signal is used to improve system computing efficiency, the high sampling-rate signal is used to improve the ASQC effect, and their conversion communication uses a hold (low to high) and a sampler (high to low). The data relating to the ASQC system was collected, and some sound quality evaluations were conducted for determining two appropriate sampling-rate values. Combining a genetic algorithm and the corresponding ASQC simulation system, the algorithmic optimal convergence coefficients and gain coefficients were further determined at different engine speeds. The verification results of ASQC using this proposed algorithm show that the loudness of interior noise is effectively suppressed; the nonlinear index is reduced to 1.37, improving by 37% relative to the original noise and by 20% compared to the ANC system.