Sound quality of low-frequency and car engine noises after active noise control

González, Alberto; Ferrer, Miguel; Diego, María de; Piñero, Gema; García-Bonito, J.

doi:10.1016/s0022-460x(02)01462-1

Cited by 133 publications

(74 citation statements)

References 17 publications

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“…The adaptation algorithms are used for the active noise and vibration control [3]. The multiple harmonics are reduced by the extension of noise sources.…”

Section: Introductionmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS) algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF) algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS) algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.

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“…The adaptation algorithms are used for the active noise and vibration control [3]. The multiple harmonics are reduced by the extension of noise sources.…”

Section: Introductionmentioning

confidence: 99%

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia

Ghosh

2018

Applied Sciences

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“…The Zwicker loudness model involves filtering the error signals to account for the transmission through the outer and middle ear, calculation of the basilar membrane excita- tion pattern, transformation of the excitation pattern to the specific loudness and finally, integration of the specific loudness over Bark gives the Loudness in Sones. This loudness model has previously been used to assess the performance of active control systems aimed at controlling automotive engine noise [8,25]. It is important to use such a psychoacoustic model when assessing the performance of active control systems, since the often employed A-weighted sound pressure level generally underestimates the loudness due to low frequency components [26], such as those present in most active control applications including the generator noise considered here.…”

Section: Performance Assessmentmentioning

confidence: 99%

Active noise control of a diesel generator in a luxury yacht

Cheer

Elliott

2016

Applied Acoustics

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Active noise control has been applied to a variety of systems in order to improve performance without the increases in size and weight that would otherwise be required by traditional passive noise control treatments. This paper investigates the application of an active noise control system to the control of generator noise in the master cabin of a luxury yacht. A multichannel, multi-tonal active noise control system employing loudspeakers and microphones in the master cabin of the yacht is investigated. It is shown that, due to the high number of engine orders produced by the generator, in order to achieve significantly perceptible levels of noise attenuation it is necessary to control at least 7 individual orders. A controller is investigated which targets 19 engine orders and it is shown to achieve in excess of 5 dB broadband attenuation, whilst achieving up to 23 dB attenuation in individual orders. This corresponds to a 23% reduction in the Zwicker loudness.

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“…One of the main reasons that affect the acoustic performance of the cab is the prominent low frequency noise which is difficult to be eliminated [1][2][3]. Previous investigation shows that the thin plates' structure radiation noise which is generated by external excitation in the frequency range of 0Hz~200Hz contributes significantly to the low frequency noise of the cab [1]. In order to reduce the noise in cab and improve vehicle acoustical comfort, the body structure that contributes tremendously to the acoustic should be optimized.…”

Section: Introductionmentioning

confidence: 99%

“…The finite element calculation method for optimization is time consuming in view of the model size. Comparatively, the approximation model optimization method is of high efficiency [1][2][3]. In paper [3], the thickness of plate was designated as the design variable.…”

Section: Introductionmentioning

confidence: 99%

Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

Qiguo

Wang

2017

MATEC Web Conf.

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Abstract. For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function) neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel's thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver's head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively .The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly. 1.INTRODUCTIONIn addition to focus on the stability and safety of driving, customers pay more attention to the acoustic characteristics of vehicle compartment. One of the main reasons that affect the acoustic performance of the cab is the prominent low frequency noise which is difficult to be eliminated [1][2][3]. Previous investigation shows that the thin plates' structure radiation noise which is generated by external excitation in the frequency range of 0Hz~200Hz contributes significantly to the low frequency noise of the cab [1]. In order to reduce the noise in cab and improve vehicle acoustical comfort, the body structure that contributes tremendously to the acoustic should be optimized. The finite element calculation method for optimization is time consuming in view of the model size. Comparatively, the approximation model optimization method is of high efficiency [1][2][3]. In paper [3], the thickness of plate was designated as the design variable. The minimum pressure at the driver's right ear and minimum body mass was defined as the optimization objective. The approximation model was established by response surface method to optimize the acoustic performance. In paper [4], the second response surface approximation model was established for the optimization of acoustic and vibration. The method of using response surface method to establish the approximation model has the characteristics of simplicity expression and high computational efficiency. However, it fails to guarantee the accuracy of the model while the number of samples is limited.This paper is organized as follows. In section 2, considering the low frequency noise of the cab, the optimal Latin Hypercube Sampling (LHS) method was used to collect sample data and the RBF neural network  Corresponding author:haqiankaka@163.com method was implemented to establish the ap...

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Sound quality of low-frequency and car engine noises after active noise control

Cited by 133 publications

References 17 publications

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Active noise control of a diesel generator in a luxury yacht

Optimization of vehicle compartment low frequency noise based on Radial Basis Function Neuro-Network Approximation Model

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