Improved Methodology of SVM to Classify Acoustic Signal by Spectral Centroid

Abstract

Acoustic signal classification issues are addressed in this work using spectral examination, channel extracting the features from the input and machine learning algorithm. This brief article examines the effect of various settings on feature extraction. This feature-level channel combination's accuracy increase is then observed. To categorise things, pattern recognition utilises a variety of classification schemes. "Pattern" refers to the measures that must be categorised with accurate feature extracted. Images and audio signals are among the most common kinds of measurements. The proposed Support Vector Machine (SVM) is used for the necessity of an effective categorization of acoustic signals driven by the continual improvements in multimedia technology. This study uses two machine learning algorithms to enhance audio classification and categorization. The proposed SVM achieves superior performance than the other ML algorithm by spectral features.