Ambulance siren noise reduction using psychoacoustic active noise control system with A-weighting filter

2015 ◽  
Author(s):  
Shajbir Singh ◽  
Manoj Kumar Sharma ◽  
Sunil Agrawal
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Noise Control ◽  
Active Noise Control ◽  
Active Noise

Effect of a tilting reflective plane on the performance of an active noise control system

2021 ◽  
pp. 107754632110317
Author(s):  
Sajaad Boodoo ◽  
Mohammad R Paurobally ◽  
Yasdeo Bissessur
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Noise Control ◽  
Single Channel ◽  
Active Noise Control ◽  
Real Life ◽  
Orientation Angle ◽  
Secondary Source ◽  
Living Room ◽  
Active Noise

This article presents the results of an investigation of the noise reduction performance of a single-channel active noise control system in the presence of a tilting reflective plane. It is shown that the noise reduction achieved by the system depends upon the orientation angle of the panel and on the separation distances between both the primary and the secondary source and the reflective panel. It is also observed that the maximum noise reduction is obtained when the reflective panel is vertical and when the separation distance is less than about 0.13 of the wavelength at the frequency of interest. When the panel is moved away, the maximum noise reduction occurs at other tilting angles. Experiments were carried out in a real living room which is close to real-life situations. It is found that there is an improvement in the extent of the quiet zone in the presence of the panel. The reduction in sound pressure level is also better with the reflective panel, which is up to 20 dB.

Psychoacoustic active noise control system based on empirical mode decomposition

2019 ◽  
Vol 67 (5) ◽  
pp. 332-349
Author(s):  
Yonghong Nie ◽  
Yu Liu ◽  
Guofeng Li ◽  
Ganqing Zhang
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Noise Control ◽  
Active Noise Control ◽  
Peak Frequency ◽  
Least Mean Square ◽  
Shaping Filter ◽  
Active Noise ◽  
Mode Decomposition

A psychoacoustic active noise control (ANC) system based on empirical mode decomposition (EMD) is proposed and implemented to improve the noise reduction performance of the control system. The noise source signal is decomposed by EMD, and the psychoacoustic parameter â–œloudnessâ–? of each intrinsic mode function (IMF) is initially calculated in such a system. Thereafter, the high-pass psychoacoustic weighting filter used to shape the error and reference signals is designed adaptively and automatically according to the loudness, peak frequency, and amplitude of each IMF. Three different ANC systems are simulated, and the sound pressure levels and loudness of their residual error signals are compared. The results demonstrate that the filter designed using this method can restrain the components of noise sources with small loudness better than the A-weighting shaping filter, so that the proposed control system can improve the noise reduction compared to those of the filtered-x least mean square and A-weighting shaping filters. Finally, the computational complexity of the three ANC systems is analyzed and compared.

scholarly journals Convex combination-based active impulse noise control system

2019 ◽  
Vol 39 (1) ◽  
pp. 190-202 ◽  
Author(s):  
Ning Yu ◽  
Zhaoxia Li ◽  
Yinfeng Wu ◽  
Renjian Feng ◽  
Bin Chen
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Impulse Noise ◽  
Noise Control ◽  
Convex Combination ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Frequency Noise ◽  
Active Noise ◽  
Risk Sensitive

Active noise control shows a good performance on the suppression of the low-frequency noise and hence it is widely applied. However, the traditional active noise control systems are unsatisfactory in controlling impulse noise in practical situations. A method based on the convex combination of filtered-x least mean square and filtered-x minimum kernel risk-sensitive loss adaptive algorithms (CFxLM) is presented to efficiently suppress impulse noise. Due to the simplicity of the LMS algorithm, the related filter is selected as the fast filter. Because the minimum kernel risk-sensitive loss algorithm is robust to impulse noise and can offer good convergence performance, we first apply it to the active noise control system and select the corresponding filter as the slow one. The proposed CFxLM algorithm can achieve both fast convergence and good noise reduction and any prior knowledge of reference noise is unnecessary. Extensive simulations demonstrate the superior noise reduction capability of the developed CFxLM-based active noise control system in controlling impulse noise.

Secondary Source Modeling Based on Analogue Circuits of Electrical Mechanical and Acoustical Systems

2011 ◽  
Vol 328-330 ◽  
pp. 2265-2269
Author(s):  
Yong Hong Nie ◽  
Jun Sheng Cheng
Keyword(s):  
Control System ◽  
Transfer Function ◽  
Noise Reduction ◽  
Noise Control ◽  
Active Noise Control ◽  
Secondary Source ◽  
Source Modeling ◽  
Ideal Model ◽  
Active Noise ◽  
Analogue Circuits

A method of secondary source modeling based on analogue circuits of electrical, mechanical and acoustical systems (SSM-ACEMAS) was provided in order to reduce the mismatch of transfer function estimation of secondary path in the simulation of active noise control (ANC) system. The convergence range and noise reduction performance of the system with SSM-ACEMAS and Filtered-X LMS algorithm were investigated. The results show that the transfer function of such a secondary source model has different amplitude and phase from the ideal model, which makes the noise reduction performance of an ANC system be much worse than the one with ideal model. Meantime, the convergence range of the control system is increased at low frequencies while decreased at high frequencies. The proposed model can provide a new method of secondary source modeling for further research on the simulation of active noise control system.

scholarly journals Active Noise Control System Based on the Improved Equation Error Model

Acoustics ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 354-363
Author(s):  
Jun Yuan ◽  
Jun Li ◽  
Anfu Zhang ◽  
Xiangdong Zhang ◽  
Jia Ran
Keyword(s):  
Control System ◽  
Noise Reduction ◽  
Mean Square Error ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Attenuation ◽  
Mean Square ◽  
Path Modeling ◽  
Active Noise ◽  
Equation Error

This paper presents an algorithm structure for an active noise control (ANC) system based on an improved equation error (EE) model that employs the offline secondary path modeling method. The noise of a compressor in a gas station is taken as an example to verify the performance of the proposed ANC system. The results show that the proposed ANC system improves the noise reduction performance and convergence speed compared with other typical ANC systems. In particular, it achieves 28 dBA noise attenuation at a frequency of about 250 Hz and a mean square error (MSE) of about −20 dB.

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