Search Results for author: Xiaoyi Shen

Found 7 papers, 2 papers with code

Unsupervised learning based end-to-end delayless generative fixed-filter active noise control

1 code implementation8 Feb 2024 Zhengding Luo, Dongyuan Shi, Xiaoyi Shen, Woon-Seng Gan

Delayless noise control is achieved by our earlier generative fixed-filter active noise control (GFANC) framework through efficient coordination between the co-processor and real-time controller.

Active Noise Control based on the Momentum Multichannel Normalized Filtered-x Least Mean Square Algorithm

no code implementations7 Aug 2023 Dongyuan Shi, Woon-Seng Gan, Bhan Lam, Shulin Wen, Xiaoyi Shen

Multichannel active noise control (MCANC) is widely utilized to achieve significant noise cancellation area in the complicated acoustic field.

Practical Active Noise Control: Restriction of Maximum Output Power

no code implementations20 Jul 2023 Woon-Seng Gan, Dongyuan Shi, Xiaoyi Shen

This paper presents some recent algorithms developed by the authors for real-time adaptive active noise (AANC) control systems.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

no code implementations20 Jun 2023 Junwei Ji, Dongyuan Shi, Woon-Seng Gan, Xiaoyi Shen, Zhengding Luo

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm.

A practical distributed active noise control algorithm overcoming communication restrictions

no code implementations15 Mar 2023 Junwei Ji, Dongyuan Shi, Zhengding Luo, Xiaoyi Shen, Woon-Seng Gan

By assigning the massive computing tasks of the traditional multichannel active noise control (MCANC) system to several distributed control nodes, distributed multichannel active noise control (DMCANC) techniques have become effective global noise reduction solutions with low computational costs.

Deep Generative Fixed-filter Active Noise Control

2 code implementations10 Mar 2023 Zhengding Luo, Dongyuan Shi, Xiaoyi Shen, Junwei Ji, Woon-Seng Gan

Due to the slow convergence and poor tracking ability, conventional LMS-based adaptive algorithms are less capable of handling dynamic noises.

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