scholarly journals Development and Evaluation of Light-Weight Active Noise Cancellation Earphones

2018 ◽  
Vol 8 (7) ◽  
pp. 1178 ◽  
Author(s):  
Sen Kuo ◽  
Yi-Rou Chen ◽  
Cheng-Yuan Chang ◽  
Chien-Wen Lai
Keyword(s):  
Noise Reduction ◽  
Real Time ◽  
Random Noise ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Experimental Results ◽  
Path Model ◽  
Light Weight ◽  
Active Noise ◽  
Natural Sound

This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.

Active Noise Control Based on TMS320C5509

2013 ◽  
Vol 631-632 ◽  
pp. 1172-1176
Author(s):  
Yong Wei Ma ◽  
Xin Ke Gou ◽  
Xian Jun Du ◽  
Chong Yu Ren
Keyword(s):  
Control System ◽  
Real Time ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Real Time Control ◽  
Time Control ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Mixed Language

The feed-forward adaptive active noise control (AANC) system is presented. Firstly, the hardware project of the system is brought forward, by selecting TMS320C5509 DSP as the controller. Then, using the mixed language, the active noise real-time control system is realized, based on the FXLMS algorithm. It’s proved that a good noise cancellation is achieved by the experiment.

Active noise control without secondary path modeling: algorithm and implementation

2021 ◽  
Vol 263 (5) ◽  
pp. 1919-1928
Author(s):  
Xing Ren ◽  
Hongwei Zhang
Keyword(s):  
Noise Reduction ◽  
Noise Control ◽  
Fitness Function ◽  
Active Noise Control ◽  
Path Model ◽  
Lms Algorithm ◽  
Abc Algorithm ◽  
Active Noise ◽  
Line Identification ◽  
Reduction Effect

Active noise control (ANC) has been intensively studied for decades. The most classical ANC algorithm should be the filtered-x least mean square (FxLMS) algorithm, which needs the model of the secondary path to work. Thus, the residual error of the ANC system is closely related to the preciseness of the secondary path model. In many applications, the secondary path is often time-varying. Therefore, off-line identification of the secondary path is not applicable. However, on-line identification often requires an additional white noise as a stimulating signal of the secondary path, which will deteriorate the final noise reduction effect. This paper proposes an improved artificial bee colony (ABC) algorithm for ANC system, which does not require identification of the secondary path. In order to guarantee the convergence of the algorithm and accelerate the convergence speed, this paper introduces a variable forgetting factor into the fitness function, and improves the traditional ABC algorithm by integrating LMS algorithm into the ABC algorithm. A single channel ANC system equipped with an FPGA hardware platform is set up in an anechoic chamber, and experiments show that the proposed algorithm can produce a satisfactory noise reduction effect without modeling the secondary path.

scholarly journals Noise cancellation system based on active noise control. 2nd Report, Adaptive control method.

1991 ◽  
Vol 57 (534) ◽  
pp. 431-436
Author(s):  
Seiichirou SUZUKI ◽  
Takurou HAYASHI ◽  
Katsuyoshi NAGAYASU ◽  
Susumu SARUTA ◽  
Hiroshi TAMURA
Keyword(s):  
Adaptive Control ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Active Noise

Coherence-based performance analysis on noise reduction in multichannel active noise control systems

2020 ◽  
Vol 148 (3) ◽  
pp. 1519-1528
Author(s):  
Jihui Aimee Zhang ◽  
Naoki Murata ◽  
Yu Maeno ◽  
Prasanga N. Samarasinghe ◽  
Thushara D. Abhayapala ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Noise Reduction ◽  
Control Systems ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

Real-Time Subspace Identification and Optimal Control for Active Noise Cancellation

2004 ◽  
Author(s):  
Harin Pongpairoj ◽  
Vineet Chaparala ◽  
Farzad Pourboghrat
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Digital Signal Processor ◽  
Adaptive Estimation ◽  
Active Noise Control ◽  
Digital Signal ◽  
Subspace Identification ◽  
Controlled System ◽  
Active Noise ◽  
Feedback Active Noise Control

In this paper, a combination of a novel recursive subspace identification and receding horizon optimal control is developed for real-time implementation, via a digital signal processor (DSP), using only input-output measurements. The proposed recursive strategy can be parameterized in terms of recursive approximation of subspace intersections and adaptive estimation of state sequences. The proposed integrated modeling-control strategy can be implemented, in real time, with minimum knowledge of the controlled system. Actual hardware experiments on feedback active noise control problem in a duct have been carried out in order to verify the performance of the proposed methodology.

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