Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Abstract

Audio classification is the reason for the multimedia gratified examination which is the utmost significant and generally utilized application these days. For huge information bases, programmed classification procedure utilizing Artificial Intelligence (AI) is more viable than the manual classification. Various sorts of AI calculations have proposed in writing like K-Nearest Neighbors Principal Component Analysis, Gaussian Mixture Model, and Hidden Markov Model, etc. By utilizing the above methods, the audio classification can be done with no class related pre-information. However, they require huge training data with no real segregation results. To beat these insufficiencies, this paper proposed a general structure for audio classification. In this paper, another audio classification algorithm utilizing Support Vector Machine (SVM) in view of Whale Optimization Algorithm (WOA) is given where WOA-SVM utilizes the class mark of the info test as the real yield. WOA is utilized for conquering the inconvenience of SVM, for example, high computational multifaceted nature as a result of the explaining of enormous scale quadratic programming in parameter iterative learning methodology. The audio sign has shown up in huge volumes on account of its tendency. With the goal that we have utilized the MapReduce approach which is one of the sorts of big data investigation to play out the classification on the unstructured information. The proposed audio classification algorithm has contrasted with a few existing classification algorithms with demonstrating its productivity and the exactness.