Identification of key audio data of belt conveyors
To address the great redundancy in the audio data of belt conveyors,an identifying method for key audio data of belt conveyors was proposed based on the improved Honey Badger Algorithm(IHBA)and optimized support vector machines(SVM).The Mel Frequency Cepstral Coefficients of the audio data were extracted as features;The Tent chaos mapping was used to increase population diversity,and new density factor and golden sine mechanism were introduced to overcome the defects of the Honey Badger Algorithm(HBA),such as easy to fall into local optimum,slow convergence speed,and low accuracy in finding the best solution.The performance of IHBA was verified through simulation experiments using standard test functions.The parameters of SVM were optimized by IHBA,and the Mel Frequency Cepstral Coefficients were input into the IHBA-SVM model for identification.The results show that the IHBA-SVM model can effectively improve the identification rate of key audio data from belt conveyors.
belt conveyoraudio dataMel Frequency Cepstral Coefficientsimproved Honey Badger Algorithmsupport vector machine