BP神经网络在扬声器异常音检测中的应用

The application of BP neural network in loudspeaker's Rub & Buzz detection

提出一种采用人工神经网络判断扬声器是否存在异常音的方法。首先简单介绍了获取扬声器异常音曲线的方法和人工神经网络中的BP模型及其训练方法,并比较了基本BP算法和共轭梯度法两种训练方法的差异。再将所获得的异常音曲线作为人工神经网络的输入向量,将听音员的听测结果作为目标向量,并使用共轭梯度法进行网络的训练。最后通过已训练好的人工神经网络判断扬声器是否存在异常音。实验结果表明,该方法可替代传统的人工设置门限的方法,并可大幅降低扬声器异常音检测的虚警率。

This paper proposes a method of using neural network to judge whether a loudspeaker is good or not. First, the method of how to obtain the Rub＆Buzz curve and the BP model including its training methods are simply introduced. Besides, the comparison between the basic BP algorithm and the conjugate gradient algorithm is also made. Then the Rub＆Buzz curve is used as the BP network’s input vector and the judgment result of experienced worker is used as the BP network’s output vector and use the conjugate gradient algorithm to train the network. Finally, the trained BP net-work can judge whether the measured loudspeaker is good or not. The experimental results show that judging a louds-peaker is good or not by a threshold, which is set up by engineer, can be replaced by artificial neural network, and the false alarm rate is greatly reduced.