Adaptive Active Vehicle Interior Noise Control Algorithm Based on Nonlinear Signal Reconstruction

2020 ◽  
Vol 39 (10) ◽  
pp. 5226-5246
Author(s):  
Xiaolan Wang ◽  
Tongzhou Wang ◽  
Lili Su ◽  
Yansong Wang ◽  
Dongpo Yang ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Noise Control ◽  
Signal Reconstruction ◽  
Interior Noise ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Nonlinear Signal

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

Sound Quality Evaluation for the Vehicle HVAC System After Active Noise Control

2008 ◽  
Author(s):  
In Hyung Yang ◽  
O. Cheol Kwon ◽  
Jung Youn Lee ◽  
Jae-Eung Oh
Keyword(s):  
Regression Model ◽  
Noise Control ◽  
Subjective Evaluation ◽  
Active Noise Control ◽  
Sound Quality ◽  
Interior Noise ◽  
Hvac System ◽  
Vehicle Interior ◽  
Active Noise ◽  
Vehicle Interior Noise

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver’s perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the HVAC sound among the vehicle interior noise has been reflected sensitively in the side of psychology. In previous study, we have developed to verify identification of source for the vehicle HVAC system through multiple-dimensional spectral analysis. Also we carried out objective assessments on the vehicle HVAC noises and subjective assessments have been already performed with 30 subjects. In this study, the linear regression models were obtained for the subjective evaluation and the sound quality metrics. The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Appropriation of regression model is necessary to R2 value and F-value. And testing for regression model is necessary to Independence, Homoscedesticity and Normality. To enhance sound quality, we applied active noise control (ANC) which is effective in the low-frequency bandwidth. Primary noise of the HVAC system is less than 500Hz. As a result of ANC application, sound quality is improved by more quiet, powerful, expensive, smooth.

A multi-source fusion algorithm for high-accuracy signal reconstruction of vehicle interior noise on passenger ear-sides

2019 ◽  
Vol 148 ◽  
pp. 75-85 ◽  
Author(s):  
Dongpo Yang ◽  
Xiaolan Wang ◽  
Yansong Wang ◽  
Hui Guo ◽  
Ningning Liu ◽  
...  
Keyword(s):  
Signal Reconstruction ◽  
High Accuracy ◽  
Interior Noise ◽  
Fusion Algorithm ◽  
Vehicle Interior ◽  
Vehicle Interior Noise
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