基于Mel频率倒谱系数和遗传算法的煤矸界面识别研究

针对现有的煤矸界面识别技术采用的γ射线法不适用于顶板不含放射性元素或者放射性元素含量较低的工作面,而雷达探测法探测范围小、信号衰减严重的问题,提出了一种基于Mel频率倒谱系数和遗传算法的煤矸界面识别方法。该方法利用煤矸放落过程中产生的声波信号的特征差异进行煤矸识别,采用Mel频率倒谱系数将去噪后的煤矸声波信号变换到频域进行处理,提取出煤矸声波信号的32维特征参数;采用遗传算法优化处理32维特征参数,得到最优参数组合;采用支持向量机和BP神经网络对最优参数进行识别。实验结果表明,该方法能够准确识别出煤矸下落状态。

Research of coal and gangue interface recognition based on Mel frequency cepstrum coefficient and genetic algorithm

In view of problems that γ ray method is not suitable for working face with no or little radioactive elements in roof and radar detection method has little detection range and serious signal attenuation which were used in current coal and gangue interface recognition technologies,the paper proposed a coal and gangue interface recognition method based on Mel frequency cepstrum coefficient and genetic algorithm.The method uses feature difference of acoustic signal produced by dropping process of coal and gangue to recognize coal and gangue.It uses Mel frequency cepstrum coefficient to process denoised acoustic signal of coal and gangue in frequency domain to extract 32 dimensions feature parameters of the acoustic signal,uses genetic algorithm to make optimal process for the parameters to get the best parameter combination,and uses support vector machine and BP neural network to recognize the best parameters.The experiment results showed that the method can recognize falling state of coal and gangue accurately.