A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on Deep Learning

Luo, Zhengding; Shi, Dongyuan; Gan, Woon‐Seng

doi:10.1109/lsp.2022.3169428

Cited by 22 publications

(5 citation statements)

References 35 publications

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“…Through the proposed algorithm, it was confirmed that the LSTM is capable of noise cancellation for diesel engine noise different from the training data (different fundamental frequency, superposed harmonic sound, engine start noise) in the linear system. Recently, there are various studies on advanced ANC algorithms using deep neural networks [14,15,26,27]. Comparing the performance of advanced ANC algorithms in practical applications is a future task.…”

Section: Anc Simulation Results and Discussionmentioning

confidence: 99%

“…Through the use of a 10-layer extended convolutional neural network (CNN) on a field-programmable gate array, a real-time streaming feedforward ANC system for in-ear headphones was shown in a practical application scenario [14]. A hybrid selective fixed-filter active noise control (SFANC) and filtered-X normalized least-mean-square (FxNLMS) approach was proposed to overcome the adaptive algorithm's slow convergence and provide a better noise reduction level using deep learning [15]. Another study presented a neural-based FxLMS with an error backpropagation algorithm to cancel non-linear broadband noise in an ANC system [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine

2022

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This study presents an active noise control (ANC) algorithm using long short-term memory (LSTM) layers as a type of recurrent neural network. The filtered least-mean-square (FxLMS) algorithm is a widely used ANC algorithm, where the noise in a target area is reduced through a control signal generated from an adaptive filter. Artificial intelligence can enhance the reduction performance of ANC for specific applications. An LSTM is an artificial neural network for recognizing patterns in arbitrarily long sequence data. In this study, an ANC controller consisting of LSTM layers based on deep neural networks was designed for predicting a reference noise signal, which was used to generate the control signal to minimize the noise residue. The structure of the LSTM neural networks and procedure for training the LSTM controller for the ANC were determined. Simulations were conducted to compare the convergence time and performances of the ANC with the LSTM controller and those with a conventional FxLMS algorithm. The noise source adopted sounds from a single-cylinder diesel engine, while reference noises selected were single harmonics, superposed harmonics, and impulsive signals generated from the diesel engine. The characteristics of each algorithm were examined through a Fourier transform analysis of the ANC results. The simulation results demonstrated that the proposed ANC method with LSTM layers showed outstanding noise reduction capabilities in narrowband, broadband, and impulsive noise environments, without high computational cost and complexity relative to the conventional FxLMS algorithm.

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Section: Anc Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine

2022

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“…The GFANC algorithm is compared to SFANC [27] and FxLMS in controlling real-world noises, which do not belong to the training dataset. The step size of FxLMS algorithm is 0.0001.…”

Section: Real-world Noise Cancellationmentioning

confidence: 99%

“…The results indicate that the control filter generated by the GFANC method is more suitable for the noise than that provided by the SFANC method. Furthermore, our previous research [27] demonstrated that combining fixed-filter and adaptive ANC algorithms can achieve rapid convergence and satisfactory noise reduction level.…”

Section: Real-world Noise Cancellationmentioning

confidence: 99%

Deep Generative Fixed-Filter Active Noise Control

Luo

Shi

Shen

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises. Selective fixed-filter active noise control (SFANC) can significantly reduce response time by selecting appropriate pre-trained control filters for different noises. Nonetheless, the limited number of pre-trained control filters may affect noise reduction performance, especially when the incoming noise differs much from the initial noises during pre-training. Therefore, a generative fixed-filter active noise control (GFANC) method is proposed in this paper to overcome the limitation. Based on deep learning and a perfect-reconstruction filter bank, the GFANC method only requires a few prior data (one pre-trained broadband control filter) to automatically generate suitable control filters for various noises. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.

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“…The controller then adjusts the control filter coefficients based on the received filter index. Notably, if the network is a 1D CNN, its input is the raw waveform [14]. However, if the network is a 2D CNN, its input is the Log Mel-spectrogram [15].…”

Section: Cnn-based Sfanc Algorithmmentioning

confidence: 99%

Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks

Luo¹,

Shi²,

Gan³

2022

Preprint

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Due to its rapid response time and a high degree of robustness, the selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use in a variety of practical active noise control (ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC can select the pre-trained control filters for different types of noise. Deep learning technologies, thus, can be used in SFANC methods to enable a more flexible selection of the most appropriate control filters for attenuating various noises. Furthermore, with the assistance of a deep neural network, the selecting strategy can be learned automatically from noise data rather than through trial and error, which significantly simplifies and improves the practicability of ANC design. Therefore, this paper investigates the performance of SFANC based on different one-dimensional and two-dimensional convolutional neural networks. Additionally, we conducted comparative analyses of several network training strategies and discovered that fine-tuning could improve selection performance.

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A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control Based on Deep Learning

Cited by 22 publications

References 35 publications

Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine

Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine

Deep Generative Fixed-Filter Active Noise Control

Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks

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