工矿自动化2025，Vol.51Issue(1)：138-144,7.DOI:10.13272/j.issn.1671-251x.2024090013
基于改进EfficientNet的煤矸音频分类方法
Coal gangue audio classification method based on improved EfficientNet
摘要
Abstract
To address the issues of severe interference of equipment operating noise and information loss caused by single extraction methods during coal gangue audio feature extraction,a coal gangue audio classification method based on improved EfficientNet is proposed.The method adopted a feature extraction approach combining Mel spectrogram and Gammatone frequency cepstral coefficients to effectively capture low-frequency information and detailed features in gangue audio.EfficientNet-B0 was selected as the backbone network,and the following improvements were made:the original multi-scale channel attention module was replaced with a convolutional block attention module,resulting in the Convolutional Attention Feature Fusion(CAFF)module.This module allowed the network to autonomously assign different weight information to features in different spatial positions,generating new effective features.Additionally,a Frequency-domain Channel Attention(FCA)module was embedded in parallel within the original MBConv module,strengthening the representation ability of feature maps and thereby improving overall network performance.The experimental results demonstrated that after introducing the CAFF module,the model's accuracy improved by 0.61％,the F1 score increased by 0.52％,and convergence was faster,indicating that the CAFF module effectively enhanced the model's ability to capture spectral features.After integrating the FCA module,accuracy improved by 0.45％,and the F1 score increased by 0.62％,showing that combining these modules further enhanced the model's generalization ability and its ability to process complex features.The improved EfficientNet model achieved an accuracy of 91.90％,with a standard deviation of 0.108,significantly outperforming other comparable audio classification models.关键词
综放开采/煤矸识别/音频特征提取/EfficientNet/Mel频谱特征/Gammatone倒谱系数/注意力机制Key words
comprehensive mining/coal gangue recognition/audio feature extraction/EfficientNet/Mel spectrogram feature/Gammatone frequency cepstral coefficient/attention mechanism分类
矿山工程引用本文复制引用
宋庆军,焦守悦,姜海燕,宋庆辉,郝文超..基于改进EfficientNet的煤矸音频分类方法[J].工矿自动化,2025,51(1):138-144,7.基金项目
国家自然科学基金面上项目(52174145) （52174145）
山东省科技型中小企业创新能力提升工程项目(2022TSGC1271,2023TSGC0620). （2022TSGC1271,2023TSGC0620）
