Proposed Active Noise Control System Research Articles

Active Noise Cancellation using a Teacher Forced BSS Learning Algorithm

In this paper, we propose a new Active Noise Control (ANC) system using a teacher forced Blind Source Separation (BSS) algorithm. The Blind Source Separation based on the Independent Component Analysis (ICA) separates the desired sound signal from the unwanted noise signal. In the proposed system, the BSS algorithm is used as a preprocessor of ANC system. Also, we develop a teacher forced BSS learning algorithm to enhance the performance of BSS. The teacher signal is obtained from the output signal of the ANC system. Computer experimental results show that the proposed ANC system in conjunction with the BSS algorithm effectively cancels only the ship engine noise signal from the linear and convolved mixtures with human voice.

Implementation of adaptive harmonic noise controller for engine exhaust system with frequency multiplier circuit

A novel active noise control (ANC) technique using adaptive frequency multiplier circuit for blade passing frequency (BPF) noise of centrifugal fan and harmonic noise of engine exhaust system is proposed. The proposed ANC system is based on the well-known adaptive controller with filtered-x least-mean-square (FXLMS) using variable step size convergence technique. The proposed controller is realised on finite impulse response (FIR) filter and implemented by using a TMS320C32 digital signal processor (DSP) with reference signal from optical fibre sensor and frequency multiplier circuit. Experiments are carried out to evaluate the noise attenuation of the proposed ANC system for centrifugal fan and engine exhaust system. The experimental results indicate that the frequency multiplier circuit could improve the performance in reducing the noise at the BPF of centrifugal fan and periodic noise of exhaust system.

An active noise control system without secondary path model

In this paper, we propose an active noise control (ANC) system without secondary path model (Ĉ filter). The proposed system is based on the simultaneous perturbation optimization method with block process. Consequently, the coefficients of the adaptive filter in the proposed system are updated by error signals only. The conventional ANC system using the Filtered-x algorithm becomes unstable due to the error between the secondary path from a secondary source to an error sensor and its model. On the other hand, the proposed ANC system has the advantage of becoming stable because of not using the model. In this paper, we show the principle of the proposed ANC system and also the effectiveness of this system by computer simulations. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(10): 47–55, 2000

Active cancellation system of acoustic noise in MR imaging.

In this paper, we introduce a new neural-network architecture for reducing the acoustic noise level in magnetic resonance (MR) imaging processes. The proposed neural network (NN) consists of two cascaded time-delay NN's (TDNN's). This NN is used as the predictor of a feedback active noise control (ANC) system for reducing acoustic noises. Experimental results with real MR noises show that the proposed system achieved an average noise power attenuation of 18.75 dB, which compares favorably with previous studies. Preliminary results also show that with the proposed ANC system installed, acoustic MR noises are greatly attenuated while verbal communication during MRI sessions is not affected.

Active Noise Control Using Fast Kalman Filter Estimation.

Active noise control (ANC) which is very effective for reduction of low-frequency noise has received much attention. However, the performance of the ANC system is generally not of a sufficiently high level for practical applications. In order to improve the performance, we propose a new feedforward ANC system which uses the fast Kalman filter algorithm. First, computer simulations are carried out to compare the conventional ANC systems which use the LMS and the LIM algorithm with the proposed ANC system. Based on the comparisons, the proposed ANC system is superior to the conventional ANC systems. Finally, experiments are carried out in an anechoic room to verify the superiority in practical use.