基于听觉特征融合的煤矸识别方法研究

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (8) : 136-144.

论文

基于听觉特征融合的煤矸识别方法研究

杨政1，王世博1,2，饶柱石3，杨善国1,2，杨建华1,2，刘送永1,2，刘后广1,2

作者信息 +

Research on coal and gangue recognition method based on auditory feature fusion

YANG Zheng1，WANG Shibo1,2，RAO Zhushi3，YANG Shanguo1,2，YANG Jianhua1,2，LIU Songyong1,2，LIU Houguang1,2

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文章历史 +

摘要

针对强噪声背景下综放开采过程中垮落煤矸难以识别问题，提出了一种融合低级听觉特征Mel频谱和高级听觉特征听觉神经递质发放率的煤矸识别方法。首先，根据煤矸垮落冲击液压支架尾梁声音信号频谱特点，基于听觉神经滤波器组模型构建了适用于煤矸识别任务的听觉模型；然后，利用听觉模型对煤矸垮落声音信号进行分析，获得听觉神经递质发放率；再次，将听觉神经递质发放率与通过Mel频谱提取的峰值特征进行融合，得到煤矸声音听觉感知图；最后，基于所构建的听觉感知图，利用ConvNeXt模型进行煤矸识别。实验结果表明，采用融合听觉特征的煤矸识别方法在不同信噪比下均具有较高的识别准确率；其优越性在背景噪声较大的工况下（信噪比为-5dB）尤为明显，准确率仍能达到91.52%，显著优于以低级听觉特征和频谱图作为识别特征和利用时频域特征结合机器学习的煤矸识别方法，验证了融合听觉特征的煤矸识别方法对噪声具有优越的鲁棒性。

Abstract

Aiming at the problem that it is difficult to recognize the caving coal gangue in the process of fully mechanized caving mining under the background of strong noise, a coal and gangue recognition method fusing low-level auditory feature Mel spectrum and high-level auditory feature auditory neurotransmitter firing rate is proposed. Firstly, according to the frequency spectrum characteristics of the sound signal of the tail beam of collapsed coal and gangue impact hydraulic support, an auditory model suitable for the coal gangue recognition task is established based on the auditory neural filter bank model. Then, the auditory model is used to analyze the sound signal of collapsed coal and gangue to obtain auditory neurotransmitter firing rate. Afterwards, the auditory neurotransmitter firing rate is fused with the peak feature extracted by Mel spectrum to obtain the auditory perception diagram of coal and gangue sound. Finally, coal and gangue were recognized with the ConvNeXt model based on the fusion auditory features constructed. The experimental results showed that the proposed coal and gangue recognition method with fusion auditory features had high recognition accuracy under different signal-to-noise ratios, and its superiority was particularly evident under the condition of large background noise (signal-to-noise ratio of -5dB), with accuracy reaching 91.52%, which was significantly superior to the method using low-level auditory features and spectrum as recognition features and using time-frequency domain features combined with machine learning, verifying the robustness of the proposed method to noise.

导出引用

杨政1，王世博1,2，饶柱石3，杨善国1,2，杨建华1,2，刘送永1,2，刘后广1,2. 基于听觉特征融合的煤矸识别方法研究[J]. 振动与冲击, 2024, 43(8): 136-144

YANG Zheng1，WANG Shibo1,2，RAO Zhushi3，YANG Shanguo1,2，YANG Jianhua1,2，LIU Songyong1,2，LIU Houguang1,2. Research on coal and gangue recognition method based on auditory feature fusion[J]. Journal of Vibration and Shock, 2024, 43(8): 136-144

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