An improved sub‐harmonic to harmonic ratio method for pitch estimation and Shadja detection

Abstract

SummaryIn this paper, we have presented an improved Sub‐Harmonic to Harmonic ratio (SHR) algorithm using the Genetic algorithm (GA) for pitch estimation of audio recordings of various ragas. Then we study the problem of Shadja identification using machine learning with the help of feature extraction and classification. The extraction of features from the raga signal is done with the help of statistical analysis of pitch estimation and these features are classified using a Neural Network (NN). Here, the training of NN is accomplished by Gray Wolf Optimization (GWO) algorithm for, determining the weights. Performance of the proposed Sa detection algorithm is analyzed by comparing the developed NN using Gray Wolf Optimization (GWO) models with the conventional models such as Levenberg Marquardt based NN (LM‐NN), Gradient Descent based (GD‐NN), Particle Swarm Optimization based NN (PSO‐NN) and FireFly based NN (FF‐NN) in terms of positive and negative performance measures.