A Wireless Reference Active Noise Control Headphone Using Coherence Based Selection Technique

Shen, Xiaoyi; Shi, Dongyuan; Gan, Woon‐Seng

doi:10.1109/icassp39728.2021.9414683

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…where C 1 (k) denotes the power spectral ratio of the specified mirror noise with respect to the actually received mirror noise, and P t,k (m) denotes multiplication of the maximum value of Ŝee,k (m) (where 0 < k < k L 2 ) when weighted by the equal-response curve A(k) [20]. If the primary sound source has weak high-frequency content, the upper limit of the allowed power spectrum of the mirror noise is given by P t,k (m).…”

Section: Data-driven Dynamic Matching Of Reconstruction Filtersmentioning

confidence: 99%

“…With the increase in local active noise control systems in practical application scenarios, more discussions about group delay and causality have been conducted [17][18][19]. In order to avoid the limitations imposed by the spatial distance on the group delay, Shen et al proposed an increase in the distance between the reference microphone and the error microphone using wireless transmission [20]. However, this method does not allow for long-frame caching and has extremely demanding delay requirements for the wireless transmission system.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Zhang,

Wu,

Wang

et al. 2024

Applied Sciences

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Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

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Section: Data-driven Dynamic Matching Of Reconstruction Filtersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Zhang,

Wu,

Wang

et al. 2024

Applied Sciences

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“…The core idea was similar to the previous work in [38], but emphasis was placed on the wireless relay design, in which an analog frequency modulator was adopted to bypass delays from digitization and processing. In 2021, Shen et al worked out a coherence-based selection algorithm of wireless reference microphones when there were two distant noise sources operating at different times [40]. In their work, the wireless relay and the motivation of employing the wireless reference microphone were fundamentally similar to the MUTE system.…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin Architecture for Wireless Networked Adaptive Active Noise Control

Shi

2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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The active noise control (ANC) is a complementary technique to the passive noise control (PNC) to reduce the low frequency noise. The ANC controller can be implemented by pre-trained filters or adaptive filters. The adaptive ANC controller is advantageous in its adaptation to environmental changes. However, the algorithm complexity of the adaptive ANC controller increases with the scale of ANC applications, making it difficult to be carried out on low-cost processors. To resolve this problem, cloud computing should be utilized in ANC systems, and thus the wireless networked ANC system is proposed. Since it is crucial for ANC controllers to generate the anti-noise wave in real time, this paper formulates a digital twin architecture that implements the control filter adaptation in the cloud and the anti-noise signal generation on the local controller, respectively. A digital twin filtered-reference least mean squares (DT-FxLMS) algorithm is proposed to coordinate the digital twin with the local controller. Simulation and experiment results demonstrate the effectiveness and efficiency of the wireless networked ANC system based on the digital twin architecture.

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“…The results of the experiment conducted in real-time indicated that ASHANC could reduce both the broadband noise and interference present in the environment and achieved a noise reduction level that was 4 − 8 dB better than the commercial headphone. mance of wireless ANC [63] and is expanded to multi-channel wireless ANC [64] in Section 5.4. A coherence-based weight determination algorithm [75] is proposed to improve the multi-channel wireless ANC in Section 5.5.…”

Section: Discussionmentioning

confidence: 99%

“…2.15. If the headphone's look-ahead time is several milliseconds, the ANC has enough time to process and generate anti-noise [63,64].…”

Section: Wireless Transmission Applied In Ancmentioning

confidence: 99%

Advanced active noise control headphone: algorithm and implementation

Shen¹

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At this time, I'd like to express my heartfelt gratitude to everyone who has helped, supported, and cared for me during these four years. I'd like to express my sincere thanks to Prof. Gan Woon Seng, my Ph.D. supervisor, for his care and support throughout my studies. His enthusiasm for research and commitment to it has profoundly impacted me. Prof. Gan created a pleasant research environment and gave me valuable life advice. Thanks to Prof. Gan's thorough guidance, I've learned much about the fun and importance of research work. I'd like to use this opportunity to express my appreciation to my senior, Dr. Shi Dongyuan, who has been a tremendous help to me since the beginning of my research. He has actively guided my studies and experiments. With his assistance, I've made significant progress. Ms. Santi Peksi assisted me in learning about local customs and doing many lab tasks. Wen Shulin (Dr), Lam Bhan(Dr), Keneth, Karn, Hai, Zhen Ting, Tho, Rishabh, Miqing, Zhengding, and Junwei were all important members and alumni of the lab throughout my Ph.D. I want to thank my three best friends in China, Zhou Yilin, Jin Jun, and Wen Jiafen, in particular. They showed me a lot of love and encouragement when I was down or upset. They also assisted me in caring for my family. Junjie, Hanxiao, Xiaofan, Mingyu, Yuncheng, Ruiyu, Yuhao, Suofei, Zhangxin, and Mengmin were among my friends in Singapore, and I am grateful for their support in my studies and daily life.Finally, I wish to express my warmest gratitude to my parents, Mr. Liu Zhen and Mrs. Shen Heyin, as well as my grandma, Mrs. Sun Qiuyun. They were always there for me no matter what I did or said. I love all of you! Shen Xiaoyi, 16 AUG 2022 xi "If I had one hour to save the world, I would spend 55 minutes defining the problem and only five minutes finding the solution."

show abstract

A Wireless Reference Active Noise Control Headphone Using Coherence Based Selection Technique

Cited by 9 publications

References 19 publications

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

Dynamic Matching of Reconstruction and Anti-Aliasing Filters in Adaptive Active Noise Control

A Digital Twin Architecture for Wireless Networked Adaptive Active Noise Control

Advanced active noise control headphone: algorithm and implementation

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