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In 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.

Summary Generally, music includes numerous genres like jazz, pop and so forth. Every genre encompasses diverse musical instruments. For classifying music manually, the individual should pay attention to the song and choose the genre. It consumes more time and the individual should know diverse genres. This article aims to introduce an innovative music genre classification model by considering 2 phases. Initially, short‐time Fourier transform, pitch, Timbre, MNMF, and proposed LBP and bag of words‐based features are extracted. Subsequently, the extracted features are subjected to ensemble classifiers, where support vector machine, neural network, random forest are deployed. The output attained from ensemble classifiers is then classified using deep belief network (DBN) which classifies the music genres in a precise way. For precise detection, the training of DBN is made optimal via tuning the weights. This optimization is carried out by a Whale Integrated Sea Lion Algorithm. At last, the supremacy of the developed model is examined via the evaluation of extant techniques.