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

Wang, Y.S.; Feng, Tianpei; Wang, X.L.; Guo, Hui; Qi, Hongxia

doi:10.1016/j.ymssp.2018.02.018

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…− Step 2: Each feature data with initial centroid is calculated using (19). The results of identification with the extraction method are calculated for the accuracy value using (20).…”

Section: K-mean Clusteringmentioning

confidence: 99%

“…Meanwhile, FN is a sample of false words, and the test results state that they are non-identical. To determine the performance of the MFCC dual-channel and MFCC single-channel methods by calculating the accuracy value by comparing TP and TN with TP, TN, FP, FN as shown in (20).…”

Section: 𝐷(𝑥mentioning

confidence: 99%

“…The noise reduction method in this study uses adaptive noise-canceling (ANC) with the least mean square (LMS) algorithm [18], [19]. This method has a simple and reliable structure [20], [21]. The structure of the LMS algorithm is shown in Figure 2(a).…”

Section: Adaptive Noise-cancelingmentioning

confidence: 99%

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Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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This paper focuses on improving the accuracy of noise audio recordings. High-quality audio recording, extraction using the mel frequency cepstral coefficients (MFCC) method produces high accuracy. While the low-quality is because of noise, the accuracy is low. Improved accuracy by investigating the effect of bandwidth on the mel scale. The proposed improvement uses the mel scale separation methods into two frequency channels (MFCC dual-channel). For the comparison method using the mel scale bandwidth without separation (MFCC single-channel). Feature analysis using k-mean clustering. The data uses a noise variance of up to -16 dB. Testing on the MFCC single-channel method for -16 dB noise has an accuracy of 47.5%, while the MFCC dual-channel method has an accuracy better of 76.25%. The next test used adaptive noise-canceling (ANC) to reduce noise before extraction. The result is that the MFCC single-channel method has an accuracy of 82.5% and the MFCC dual-channel method has an accuracy better of 83.75%. High-quality audio recording testing for the MFCC single-channel method has an accuracy of 92.5% and the MFCC dual-channel method has an accuracy better of 97.5%. The test results show the effect of mel scale bandwidth to increase accuracy. The MFCC dual-channel method has higher accuracy.

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“…− Step 2: Each feature data with initial centroid is calculated using (19). The results of identification with the extraction method are calculated for the accuracy value using (20).…”

Section: K-mean Clusteringmentioning

confidence: 99%

Section: 𝐷(𝑥mentioning

confidence: 99%

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Feature extraction with mel scale separation method on noise audio recordings

Huizen

Kurniati

2021

IJEECS

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“…One can combine the ANE structure with different adaptive control algorithms to achieve the control goal of sound quality [11]. For example, a post-masking least mean square (LMS) algorithm was presented to achieve effective sound quality control aimed at vehicle interior noise [12]. A broadband self-tuning ANE system utilizing the filtered-x least mean square (FxLMS) algorithm was researched in Reference [13].…”

Section: Introductionmentioning

confidence: 99%

Modified Structures for Hybrid Active Sound Quality Control System Disturbed by Gaussian Random Noise

Jin

et al. 2020

Applied Sciences

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A hybrid active sound quality control system, in which a hybrid feedforward and feedback structure is applied, can not only be used in cases where the line-spectrum noise is obtained easily with reference sensors, but it can also improve the comfortability of noise and eliminate unexpected Gaussian random noise. However, the traditional structure for a hybrid active sound quality control system, whereby a reference signal in the feedback control structure is synthesized by the output signals of the feedforward control filter, feedback control filter, and line-spectrum noise cancellation control filter, introduces couplings of the three control filters. To remove the coupling interactions of the feedforward and feedback control structures and to reduce the complexity of the control system, two modified structures with less computational complexity or a smaller increase in computation are investigated in this paper. The first one involves a simplified structure in which the reference signal in the feedback control structure is replaced by the summation of the residual error signal and the output signal of the line-spectrum noise cancellation control filter, and the second one is a modified structure which integrates the output signals of the feedback control filter and the line-spectrum noise cancellation control filter for the reference signal in the feedback control structure. Numerical simulations are carried out to show the performance of the modified structures. The results illustrate that the two modified structures have the ability to cancel Gaussian random noise and to reduce or enhance the amplitude of line-spectrum noise to promote sound quality. Moreover, a simplified structure with a new leaky filtered-x least mean square (FxLMS) algorithm is proposed to upgrade the noise reduction performance and elevate stability in the feedback control structure. The effectiveness of the proposed algorithm also is proven by the simulation results.

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“…Active control of the interior road noise of a car has been researched for over two decades, and a variety of active noise control (ANC) techniques have been proposed to improve ride comfort [4][5][6]. Sutton et al proved that at least six vibrational reference signals are required to attenuate broadband road noise by using a filtered-x least mean square (FXLMS) algorithm [4].…”

Section: Introductionmentioning

confidence: 99%

Active Control of Broadband Noise Inside a Car Using a Causal Optimal Controller

Zhu

Yang

et al. 2019

Applied Sciences

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This paper investigates active broadband noise control inside vehicles with a multichannel controller. The noncausal inversion of a practical nonminimum-phase secondary path is formulated, and its influence on noise-reduction performance is analyzed. Based on multiple coherence between reference signals and undesired noise, a novel formulation for identifying primary paths with correlated excitation signals is presented and a causal optimal controller is proposed. Meanwhile, the proposed controller can be used as an accurate predictor to estimate the maximal achievable noise reduction and provide a reference to improve the control systems. The robustness of the proposed algorithm is examined by varying the uncertainty of primary paths. Finally, the performance of the proposed causal optimal controller is validated using the data measured in a car. The results show that the proposed algorithm outperforms traditional algorithms and achieves a significant broadband noise reduction in time-invariant systems.

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

Cited by 22 publications

References 28 publications

Feature extraction with mel scale separation method on noise audio recordings

Feature extraction with mel scale separation method on noise audio recordings

Modified Structures for Hybrid Active Sound Quality Control System Disturbed by Gaussian Random Noise

Active Control of Broadband Noise Inside a Car Using a Causal Optimal Controller

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