Improvement of impact noise in a passenger car utilizing sound metric based on wavelet transform

Lee, Sang Kwon; Kim, Howuk; Na, Eun-Woo

doi:10.1016/j.jsv.2010.03.001

Cited by 38 publications

(16 citation statements)

References 9 publications

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“…For the sound metric, four psychoacoustic features [14] were calculated, including the loudness, sharpness, roughness, and fluctuation strength. These four psychoacoustic features have been used for sound quality evaluations in the field of automotive engineering [7,8,5,9,4,10].…”

Section: Pulling Soundmentioning

confidence: 99%

“…It was concluded that these perceptions were influenced by the width, comfort, hardness, and stiffness of the wings of a seat. Many studies have also been conducted on the perception of sound quality of cars [4,7,9,10]. Kim et al [4] introduced the new tonality as a new metric for the objective perception of gear whine noise.…”

Section: Introductionmentioning

confidence: 99%

“…They used the artificial neural network. Lee et al [9] proposed the impulsiveness as a new metric for the objective perception of impact noise in a vehicle based on wavelet transform. This is useful for objective evaluation of the subjective rating of impact noise.…”

Section: Introductionmentioning

confidence: 99%

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Development of an index for the sound and haptic quality of a seat belt

et al. 2015

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Section: Pulling Soundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of an index for the sound and haptic quality of a seat belt

et al. 2015

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“…The time-frequency analysis algorithms, such as the short-time Fourier transform (STFT), discrete wavelet transform (DWT), wavelet packet analysis (WPA) and the Wigner-Ville distributions (WVD), were introduced into SQE engineering for sound feature extraction (virtual "ear") of the impact and nonstationary vehicle noises. [20][21][22] Originally used for signal processing, wavelet techniques have been developed and successfully applied in structure analysis, diagnosis of crack faults and wave propagation in structures. [39][40][41][42][43][44] Due to good time-frequency characteristics, the wavelet-based algorithms are usually considered for SQE of both stationary and nonstationary noises.…”

Section: Introductionmentioning

confidence: 99%

Roughness Evaluation Approach for Nonstationary Vehicle Noise Based on Wavelet Packet and Neural Network Techniques

Guo¹,

Wang²,

Wang³

et al. 2018

IJAV

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Based on the wavelet packet decomposition (WPD) and artificial neural network (ANN) methods, this paper presents a new technique for auditory roughness evaluation (ARE) of nonstationary vehicle noise, named WPD-ANN-ARE model. According to sound transfer and perception by the human auditory system, the noise roughness of a sample vehicle under different conditions of constant speed, acceleration, and braking are calculated. After comparisons by the time-frequency analysis techniques in common use, a WPD model with approximately twentyone critical bands, which is specially designed by considering the auditory perception characteristics of human, is proposed for envelope feature extraction of vehicle noise. Taking the WPD extracted features as inputs and the calculated roughnesses as outputs, a back-propagation ANN with one hidden layer is trained and established for ARE of nonstationary vehicle noises. The verification results show that errors of the time-varying roughness calculated from the WPD-ANN-ARE are below 8.56 percent, which suggest a very good accuracy of the newly proposed ARE model. In applications, the WPD-ANN-ARE can be directly used in ARE of vehicle noises. And the modelling approach presented in this paper may be extended to other sound related fields for sound quality evaluation (SQE) in engineering.

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“…Bi Fengrong et al [3] established the least squares support vector machine model on the basis of psychoacoustic objective parameters and EEMD signal features, thereby conducting the research of the acoustic quality of diesel engine radiated noise. Lee et al [4] proposed the wavelet transform-based evaluation parameters of impact sound quality HFEC and the objective parameters with both roughness and volatility as multivariate linear regression model, which were applied to predict the acoustic quality of suspension system components.…”

Section: Introductionmentioning

confidence: 99%

Study on the Sound Quality of Steady and Unsteady Exhaust Noise

Fa-lin

Sun

2018

Mathematical Problems in Engineering

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In order to predict and study the sound quality of automobile exhaust noise, Zwicker steady-state and time-varying method were applied to calculate the psychoacoustic objective parameter values in terms of the exhaust noise of sample cars at uniform velocity and accelerated velocity; Thereby, a prediction model of GA-BP sound quality based on psychoacoustic objective parameters was established. At the same time, wavelet analysis was used to decompose the accelerated signal; in order to overcome the shortcomings such as Heisenberg uncertainty, the RNR (regularization nonstationary regression technique) was applied to compute the WVD distribution (RNR-WVD), therefrom obtaining the coefficient matrices of different-band signals after wavelet decomposition, and then A weighting was carried out on the coefficient matrices, so as to establish a new sound quality parameter SQP-WRW (sound quality parameter base on wavelet and then proceed to RNR-WVD) as the input of GA-BP model, and therefrom a sound quality prediction model was established. The results indicate that the model based on SQP-WRW has higher precision for predicting the sound quality of acceleration signal, and it can better reflect the characteristics of acceleration signal and sound quality.

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Improvement of impact noise in a passenger car utilizing sound metric based on wavelet transform

Cited by 38 publications

References 9 publications

Development of an index for the sound and haptic quality of a seat belt

Development of an index for the sound and haptic quality of a seat belt

Roughness Evaluation Approach for Nonstationary Vehicle Noise Based on Wavelet Packet and Neural Network Techniques

Study on the Sound Quality of Steady and Unsteady Exhaust Noise

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