Acoustic Tuning of Lightweight Vehicle Interior Systems

Abstract Vehicle interior noise is the result of numerous sources of excitation. One source involving tire pavement interaction is the tire air cavity resonance and the forcing it provides to the vehicle spindle: This paper applies fundamental principles combined with experimental verification to describe the tire cavity resonance. A closed form solution is developed to predict the resonance frequencies from geometric data. Tire test results are used to examine the accuracy of predictions of undeflected and deflected tire resonances. Errors in predicted and actual frequencies are shown to be less than 2%. The nature of the forcing this resonance as it applies to the vehicle spindle is also examined.

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

Noise Control Engineering Journal ◽

10.3397/1/376737 ◽

2019 ◽

Vol 67 (6) ◽

pp. 405-414 ◽

Cited By ~ 2

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.

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Sound intensity transfer path analysis and its application to analyze the acoustics in vehicle interior

Noise Control Engineering Journal ◽

10.3397/1/376619 ◽

2018 ◽

Vol 66 (3) ◽

pp. 222-230 ◽

Cited By ~ 2

Author(s):

Yosuke Tanabe ◽

Akira Inoue

Keyword(s):

Path Analysis ◽

Sound Intensity ◽

Vehicle Interior ◽

Transfer Path ◽

Transfer Path Analysis ◽

Intensity Transfer

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Squeak and rattle noise classification using radial basis function neural networks

Noise Control Engineering Journal ◽

10.3397/1/376824 ◽

2020 ◽

Vol 68 (4) ◽

pp. 283-293

Author(s):

Oleksandr Pogorilyi ◽

Mohammad Fard ◽

John Davy ◽

Mechanical and Automotive Engineering, School ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Accuracy ◽

Training Method ◽

Vehicle Interior ◽

Trained Classifier ◽

Different Types ◽

Noise Classification ◽

Automatic Tool ◽

Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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Video Analytic Based Health Monitoring for Driver in Moving Vehicle by Extracting Effective Heart Rate Inducing Features

Journal of Advanced Transportation ◽

10.1155/2018/8513487 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Kanghyu Lee ◽

David K. Han ◽

Hanseok Ko

Keyword(s):

Heart Rate ◽

Estimation Method ◽

Region Of Interest ◽

Ensemble Empirical Mode Decomposition ◽

Superior Performance ◽

Indoor Environments ◽

Vehicle Interior ◽

Moving Vehicle ◽

Ambient Lighting ◽

Driving Conditions

We propose a novel remote heart rate (HR) estimation method using facial images based on video analytics. Most of previous methods have been demonstrated in well-controlled indoor environments. In contrast, this paper proposes a practical video analytic framework under actual driving conditions by extracting key HR inducing features. In particular, when cars are driven, effective and stable HR estimation becomes challenging as there are many dynamic elements, such as rapid illumination changes, vibrations, and ambient lighting that can exist in the vehicle interior. To overcome those disturbances of HR estimation, the driver face region is first detected and cropped to the region of interest (RoI). Second, the components related to HR are extracted from mixed noisy components using ensemble empirical mode decomposition (EEMD). Finally, the extracted signal is analyzed in frequency domain and smoothed with temporal filtering. To verify our approach, the proposed method is compared with recent prominent methods employing a public HCI dataset. It has been demonstrated that the proposed approach delivers superior performance under driving conditions using Bland-Altman plots.

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