基于FXLMS算法的矿用有源耳罩的研究

基于矿井中低频噪声突出，存在时间长、危害大，尤其周期性噪声比重较大，而传统降噪技术对其降噪效果并不明显，提出了基于fxlms 算法的自适应有源降噪耳罩降低矿井中的低频周期性信号，并采用离线建模方法建立了次级通道模型，利用MATLAB8.0拟合了64阶的滤波器，对 矿井中具有代表性的1kHz低频周期噪声和伴有随机噪声的1kHz低频周期性噪声进行了降噪仿真研究。结果表明:1kHz低频周期性噪声的降噪量 达到了30dB。而且在本模型下，随机噪声对低频周期性噪声的降噪效果几乎没有影响。

Study on mining active earmuff based on FXLMS algorithm

The low-frequency noise is outstanding in mine, and its existence time is long, leading to great harm. Especially the periodic noise holds a larger proportion in it, and the noise reduction effect of traditional noise reduction technology is not obvious. Hence, an adaptive active noise control earmuff (ANCE) based on filtered-x least mean squares (FXLMS) was put forward to reduce the low-frequency periodic signal in mine. The secondary path modeling was established by using offline modeling method, and a 64 order filter was fitted by MATLAB 8.0. The simulation study of noise reduction was conducted on 1kHz low frequency periodic noise and 1kHz low frequency periodic noise with random noise, which are typical in mine. The results showed that the amount of noise reduction reached 30 dB, and the random noise has almost no influence on noise reduction effect of low frequency periodic noise under this model.