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

• URL: http://arxiv.org/abs/2208.08440v1
• Date: Wed, 17 Aug 2022 05:47:38 GMT
• Title: Performance Evaluation of Selective Fixed-filter Active Noise Control based on Different Convolutional Neural Networks
• Authors: Zhengding Luo, Dongyuan Shi, Woon-Seng Gan
• Abstract summary: The selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use. Deep learning technologies can be used in SFANC methods to enable a more flexible selection of the most appropriate control filters. This paper investigates the performance of SFANC based on different one-dimensional and two-dimensional convolutional neural networks.
• Score: 19.540619271798455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Many-Objective Search-Based Coverage-Guided Automatic Test Generation for Deep Neural Networks [2.141511605027007]
  This paper proposes a fuzzing test generation technique based on many-objective optimization algorithms. The frequency-based fuzz sampling strategy assigns priorities based on the frequency of selection of initial data. A local search strategy based on the Monte Carlo tree search is proposed to enhance the algorithm's local search capabilities.
  arXiv  Detail & Related papers  (2024-11-01T21:08:15Z)
• Regime Learning for Differentiable Particle Filters [19.35021771863565]
  Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference. No prior approaches effectively learn both the individual regimes and the switching process simultaneously. We propose the neural network based regime learning differentiable particle filter (RLPF) to address this problem.
  arXiv  Detail & Related papers  (2024-05-08T07:43:43Z)
• Combating Label Noise With A General Surrogate Model For Sample Selection [84.61367781175984]
  We propose to leverage the vision-language surrogate model CLIP to filter noisy samples automatically. We validate the effectiveness of our proposed method on both real-world and synthetic noisy datasets.
  arXiv  Detail & Related papers  (2023-10-16T14:43:27Z)
• Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters [69.24377241408851]
  Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. We propose to base the selection on gradient-signal-to-noise ratio (GSNR) of network's parameters.
  arXiv  Detail & Related papers  (2023-10-11T10:21:34Z)
• Thompson sampling for improved exploration in GFlowNets [75.89693358516944]
  Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
  arXiv  Detail & Related papers  (2023-06-30T14:19:44Z)
• Deep Generative Fixed-filter Active Noise Control [17.42035489262148]
  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. The efficacy of the GFANC method is demonstrated by numerical simulations on real-recorded noises.
  arXiv  Detail & Related papers  (2023-03-10T08:47:22Z)
• A Hybrid SFANC-FxNLMS Algorithm for Active Noise Control based on Deep Learning [17.38644275080562]
  Filtered-X normalized least-mean-square (FxNLMS) algorithm can obtain lower steady-state errors through adaptive optimization. This paper proposes a hybrid SFANC-FxNLMS approach to overcome the adaptive algorithm's slow convergence. Experiments show that the hybrid SFANC-FxNLMS algorithm can achieve a rapid response time, a low noise reduction error, and a high degree of robustness.
  arXiv  Detail & Related papers  (2022-08-17T05:42:39Z)
• Deep Frequency Filtering for Domain Generalization [55.66498461438285]
  Deep Neural Networks (DNNs) have preferences for some frequency components in the learning process. We propose Deep Frequency Filtering (DFF) for learning domain-generalizable features. We show that applying our proposed DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks.
  arXiv  Detail & Related papers  (2022-03-23T05:19:06Z)
• Study of Frequency domain exponential functional link network filters [0.0]
  We propose a novel frequency domain exponential functional link network (FDEFLN) filter to improve the computational efficiency. A FDEFLN-based nonlinear active noise control (NANC) system has also been developed to form the frequency domain exponential filtered-s least mean-square (FDEFsLMS) algorithm.
  arXiv  Detail & Related papers  (2022-01-12T01:54:59Z)
• Capturing scattered discriminative information using a deep architecture in acoustic scene classification [49.86640645460706]
  In this study, we investigate various methods to capture discriminative information and simultaneously mitigate the overfitting problem. We adopt a max feature map method to replace conventional non-linear activations in a deep neural network. Two data augment methods and two deep architecture modules are further explored to reduce overfitting and sustain the system's discriminative power.
  arXiv  Detail & Related papers  (2020-07-09T08:32:06Z)
• Ensemble Wrapper Subsampling for Deep Modulation Classification [70.91089216571035]
  Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems.
  arXiv  Detail & Related papers  (2020-05-10T06:11:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.