An improved LMS algorithm for active sound-quality control of vehicle interior noise based on auditory masking effect

• Published in: Mechanical systems and signal processing Vol. 108; pp. 292 - 303
• Main Authors: Wang, Y.S., Feng, T.P., Wang, X.L., Guo, H., Qi, H.Z.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.08.2018; Elsevier BV
• Subjects: Active noise control; Active sound-quality control; Adaptive algorithms; Adaptive control; Algorithms; Auditory masking; Automotive parts; Computer simulation; LMS algorithm; Loudness; Masking; Passenger comfort; Post-masking effect; Quality control; Signal processing; Signal to noise ratio; Sound; Vehicle interior noise; Vehicle interior noise; LMS algorithm; Active sound-quality control; Post-masking effect
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2018.02.018

•An improved active sound-quality control (ASQC) method is presented.•A novel ASQC concept based on human auditory characteristics is proposed.•A new algorithm called PmLMS is developed based on the post-masking effect.•The PmLMS properties for ASQC of vehicle interior noise are discussed.•The ASQC results of the PmLMS are better than those of the conventional LMS. Based on the adaptive least mean square (LMS) algorithm commonly used in active noise control (ANC), an improved active sound-quality control (ASQC) method for vehicle interior noise, so-called post-masking LMS (PmLMS) algorithm, is presented in this paper. Aiming at sound loudness index of measured vehicle interior noises, the PmLMS is derived by considering the post-masking effect of human auditory system. Through adjusting the sizes of iteration step in simulations, it is proven that the newly proposed PmLMS has similar properties as those of the LMS. Comparisons of simulation experiment show that, under the same conditions of appropriate iteration step, filter order and target noise signal, the ASQC results from the PmLMS are better than those from the LMS algorithm, which suggests an effective control of vehicle interior noise. In applications, if one may reasonably match the size of iteration step and vehicle running speed, the PmLMS algorithm can be directly used in ASQC system of a vehicle for improving the ride comfort of passengers. The proposed PmLMS algorithm as a promising method may be further extended to the filtered-x LMS (FxLMS) and applied in other ANC fields for sound quality control in engineering.