A Chaotic Gradient-based Optimization with Support Vector Machine for Chinese Folk Music Classification

Nowadays, Chinese folk music classification has a huge attraction on the researches end where traditional Chinese folk music classification has difficulty with feature selection, and current Machine Learning (ML) algorithms have limitations such as poor classification accuracy. Conventional music classification algorithms struggle to accurately classify Chinese folk music due to its sensitive rhythms and tone changes. The goal is to develop algorithms that can accurately recognize and classify diverse folk tunes in the Chinese music collection. So, Chaotic Gradient-Based Optimizer (CGBO) is introduced that merges chaotic maps with GBO searching rounds to find the most discriminating features and finally for folk music categorization, the Support Vector Machine (SVM) algorithm is used. In this research to evaluate the performance of the proposed CGBO-SVM, input data is created by self as set of ethnic music data, this data collection includes 10 different genres. Every genre includes minimum count of 100 samples, and every sample lasting 30 secs with sampling rate of 22050 Hz, while the One Note detector technique is utilized to effectively extract features. The experimental results reveal that the suggested CGBO-SVM achieve 99.96% of classification accuracy and performs better than existing approaches such as K-Nearest Neighbor (KNN) and Bi-Directional Long Short Term Memory (Bi-LSTM).