Hybrid time–frequency algorithm for active sound quality control of vehicle interior noise based on stationary discrete wavelet transform

Abstract

Active sound quality control (ASQC) of vehicle interior noise is to reshape the sound spectrum by considering human perception. But current ASQC method mainly controls the interior noise amplitude, ignoring the time–frequency characteristics, and active control of sharpness is a problem. According to psychoacoustic indices, the original noise needs to be decomposed into subbands to obtain targeted control. Thus, this study presents a hybrid time–frequency domain active noise equalization (TFD-ANE) algorithm based on stationary discrete wavelet transform. For get a supporting effect, a variable step-size FxLMS (VS-FxLMS) and a normalized frequency-domain block FxLMS are inspected and determined for each subband noise suppression. It can be found NFB-FxLMS can control higher frequency noise well, while VS-FxLMS just shows better control effect on the lowest frequency subband. To verify the effectiveness of proposed method, ASQC simulations are performed by using the basic ANE, VS-ANE, NFB-ANE and TFD-ANE algorithms. Moreover, three psychoacoustic indices, loudness, sharpness, and roughness, with the utmost impact on the interior sound quality, are considered to quantify the noise improvement effect. Results show that TFD-ANE achieves the optimum performance, where the loudness and roughness are further reduced by more than 38% and 46% compared with other three algorithms. In terms of sharpness, the ANE and VS-ANE are ineffective, and the NFB-FxLMS cannot improve the sharpness of nonstationary interior noise. However, the proposed TFD-ANE can reduce the sharpness by approximately 40%, which suggests a promising approach to the ASQC of vehicle interior noises and other sound-related fields in engineering.