Latest Advances in Active Noise Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 12381

Special Issue Editors


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Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: active noise control; adaptive filtering; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Interests: active noise control; adaptive signal processing; psycho-acoustical signal processing; spatial/3D audio processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website1 Website2
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology, Chengdu 611731, China
Interests: signal and information processing

Special Issue Information

Dear Colleagues

We have seen recent exponential growth in active noise control (ANC) applications, especially in commercial headphones and hearing devices, and engine and road noise control in the automobile. These commercial successes have also spurred academic and industry interest in investigating the application of new signal processing and artificial intelligence techniques to take on more complex and dynamic noise control problems in the real world. Furthermore, with the advancement of low-cost, fast-computation, and low-power embedded processors, which can implement more complex multi-channel adaptive signal processing algorithms in real-time, and coupled with reliable sensors and actuators, we are expecting to see more innovative products that harness the power of ANC to make quieter machinery, appliances, and, eventually, quiet down our living and working environments. We are also witnessing huge advancements in the latest machine and deep learning techniques in solving and continually improving many AI-based sound systems, and there are huge opportunities to bring these advanced techniques into the ANC applications. For this Special Issue, we are inviting the latest research findings, new approaches, and applications of the “Latest Advances in Active Noise Control” to better mitigate acoustical noise in a more efficient manner and approach.

Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Active noise control algorithms
  • Applying machine learning / deep learning in active noise control
  • Integration of active noise control subsystems
  • New applications of active noise control
  • New techniques in active noise control headphones, automobiles, and spaces
  • Sensor and actuator
  • Sound masking for active noise control
  • Virtual sensing and noise control
  • Real-time implementation of the novel active control technique

Dr. Dongyuan Shi
Prof. Dr. Woon-Seng Gan
Dr. Chuang Shi
Guest Editors

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Keywords

  • active noise control algorithms
  • applying machine learning / deep learning in active noise control
  • integration of active noise control subsystems
  • new applications of active noise control
  • new techniques in active noise control headphones, automobiles, and space
  • sensor and actuator
  • sound masking for active noise control
  • virtual sensing and noise control
  • real-time implementation of the novel active control technique

Published Papers (5 papers)

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Research

13 pages, 1097 KiB  
Article
A Convolution-Neural-Network Feedforward Active-Noise-Cancellation System on FPGA for In-Ear Headphone
by Young-Jae Jang, Jaehyun Park, Won-Cheol Lee and Hong-June Park
Appl. Sci. 2022, 12(11), 5300; https://doi.org/10.3390/app12115300 - 24 May 2022
Cited by 4 | Viewed by 3967
Abstract
A real-time streaming feedforward active-noise-cancellation (ANC) system for an in-ear headphone was demonstrated in a real application scenario, by implementing a 10-layer dilated convolutional-neural-network (CNN) on a field programmable gate array (FPGA). A 16 × 16 systolic array was used in the FPGA, [...] Read more.
A real-time streaming feedforward active-noise-cancellation (ANC) system for an in-ear headphone was demonstrated in a real application scenario, by implementing a 10-layer dilated convolutional-neural-network (CNN) on a field programmable gate array (FPGA). A 16 × 16 systolic array was used in the FPGA, to speed up the model computation. The system latency was 170.6 μs, at the system clock frequency of 120 MHz. The CNN model used 3232 parameters. Due to the large input receptive field, of 327 ms, this work achieved total power reduction, of 14.8 dB and 14.3 dB at the noise incident direction of 0 and 90, respectively, and the noise attenuation bandwidth was 2000 Hz at both angles; all results were superior to those of the conventional FxLMS algorithm. Full article
(This article belongs to the Special Issue Latest Advances in Active Noise Control)
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17 pages, 11089 KiB  
Article
Enhancing Engine Order Sound Using Additive Feedforward Control for a Secondary Path with Uncertainty at Higher Frequencies
by Seokhoon Ryu, Jihea Lim, Young-Sup Lee, Eunsuk Yoo and Chasub Lim
Appl. Sci. 2022, 12(9), 4486; https://doi.org/10.3390/app12094486 - 28 Apr 2022
Cited by 1 | Viewed by 1189
Abstract
An active sound profiling (ASP) control algorithm based on the command FxLMS with an additive feedforward enhancement is suggested to overcome the limited performance to track a pre-defined target sound in a car cabin when an electronic sound generator (ESG) attached to a [...] Read more.
An active sound profiling (ASP) control algorithm based on the command FxLMS with an additive feedforward enhancement is suggested to overcome the limited performance to track a pre-defined target sound in a car cabin when an electronic sound generator (ESG) attached to a cowl panel of a car was adopted as a secondary actuator. As the uncertainty of the secondary path including the ESG-cowl pair was increased especially at higher frequencies, the tracking performance of a pure ASP algorithm was limited. The feedforward enhancement was added to the pure ASP algorithm to allow a robust tracking performance against a pre-defined target sound to enrich the insufficient engine sound at higher engine orders. After implementing this additive ASP approach in a test car, a real-time control experiment was carried out to demonstrate its tracking performance. A target sound was defined to cancel three engine order noises and to enhance the other three order sounds in the experiment. The experiment results showed that the proposed approach was able to provide an improved robust tracking performance at the engine orders for enhancing sound at higher frequencies compared to the pure ASP algorithm. Full article
(This article belongs to the Special Issue Latest Advances in Active Noise Control)
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13 pages, 2367 KiB  
Article
A Novel PSO-Based Adaptive Filter Structure with Switching Selection Criteria for Active Noise Control
by Eduardo Pichardo, Esteban Anides, Angel Vazquez, Eduardo Vazquez, Juan C. Sánchez, Héctor M. Pérez, Gabriel Sánchez, Juan G. Avalos and Giovanny Sánchez
Appl. Sci. 2022, 12(9), 4368; https://doi.org/10.3390/app12094368 - 26 Apr 2022
Viewed by 1674
Abstract
In recent years, active noise control (ANC) systems have been widely used in advanced electronic appliances. Nowadays, several authors use gradient-optimization algorithms since they can be easily implemented in these devices. However, these algorithms need to estimate the secondary path in advance. As [...] Read more.
In recent years, active noise control (ANC) systems have been widely used in advanced electronic appliances. Nowadays, several authors use gradient-optimization algorithms since they can be easily implemented in these devices. However, these algorithms need to estimate the secondary path in advance. As consequence, this factor can limit its use in real-ANC applications since the secondary path can undergo significant variations over time. To solve this problem, we propose an ANC system with switching filter selection based on particle swarm optimization (PSO) algorithms. Specifically, we use two sets of populations of particles with different acceleration coefficients and inertia weights to create an advanced structure in which the first PSO algorithm guarantees a high convergence speed while the use of the second PSO algorithm allows to achieve a high-level noise reduction. The results demonstrate that the proposed algorithm exhibits better convergence properties compared with previously reported solutions. Full article
(This article belongs to the Special Issue Latest Advances in Active Noise Control)
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14 pages, 2034 KiB  
Article
A Feasibility Study of an ESG to Suppress Road Noise of a Car
by Young-Sup Lee, Seokhoon Ryu, Eunsuk Yoo and Chasub Lim
Appl. Sci. 2022, 12(5), 2697; https://doi.org/10.3390/app12052697 - 04 Mar 2022
Cited by 3 | Viewed by 1638
Abstract
This study considered implementing an active road noise control (ARNC) system using an electronic sound generator (ESG) as a secondary actuator to suppress road noise in a car cabin. The ESG was installed to the cowl panel of a test car to generate [...] Read more.
This study considered implementing an active road noise control (ARNC) system using an electronic sound generator (ESG) as a secondary actuator to suppress road noise in a car cabin. The ESG was installed to the cowl panel of a test car to generate structure-borne anti-noise by vibrating the panel. A robust multiple-reference single-input single-output (MR-SISO) ARNC algorithm based on the FxLMS was designed. Four 3-axis accelerometers and a microphone were adopted to acquire the reference signals and the error signal for the control algorithm. The radiated sound pressure from the ESG–cowl pair was high enough to suppress the road noise at a car speed of 60 kph. The optimized least number of reference signals and their locations were determined after computer simulation from the measured primary path data. Real-time control experiments showed an A-weighted sound pressure level reduction of 6.0 dB in the average of three dominant road booming noises in 100–250 Hz with the four optimized reference signals at 60 kph. More reference signals gave a further reduction such as 8.3 dB with 12 reference signals. Thus, this study suggests that the ESG coupled with the cowl panel can be an affordable alternative as a secondary actuator in an ARNC system to suppress road noise in a car. Full article
(This article belongs to the Special Issue Latest Advances in Active Noise Control)
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19 pages, 7487 KiB  
Article
An Alternative Approach to Obtain a New Gain in Step-Size of LMS Filters Dealing with Periodic Signals
by Pedro Ramos Lorente, Raúl Martín Ferrer, Fernando Arranz Martínez and Guillermo Palacios-Navarro
Appl. Sci. 2021, 11(12), 5618; https://doi.org/10.3390/app11125618 - 17 Jun 2021
Cited by 3 | Viewed by 1472
Abstract
Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved [...] Read more.
Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved as a valid strategy in the active control of periodic noise consisting of a sum of harmonics. The convergence analysis carried out here is based on the periodic nature of the input signal, which makes it possible to formulate the adaptive process with a matrix-based approach, the periodic least-mean-square (P-LMS) algorithm In this paper, we obtain the upper bound that limits the step-size parameter of the sequential PU P-LMS algorithm and compare it to the bound of the full-update P-LMS algorithm. Thus, the limiting value for the step-size parameter is expressed in terms of the step-size gain of the PU algorithm. This gain in step-size is the quotient between the upper bounds ensuring convergence in the following two scenarios: first, when PU are carried out and, second, when every coefficient is updated during every cycle. This step-size gain gives the factor by which the step-size can be multiplied so as to compensate for the convergence speed reduction of the sequential PU algorithm, which is an inherently slower strategy. Results are compared with previous results based on the standard sequential PU LMS formulation. Frequency-dependent notches in the step-size gain are not present with the matrix-based formulation of the P-LMS. Simulated results confirm the expected behavior. Full article
(This article belongs to the Special Issue Latest Advances in Active Noise Control)
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