A classification study for Turkish folk music makam recognition using machine learning with data augmentation techniques.

Makam is defined as melodies that are described with typical the agâz(beginning), seyir (the orientation style), and karar (ending) features in a certain perde düzen (tone/fret tunings). For this reason, determining the makam is the basic step in understanding the melodic progression, as in musical keys. Machine learning, an artificial intelligence discipline, offers stable solutions for estimating different situations based on known data. In this study, classifications for makam recognition in Turkish folk music were carried out using different machine learning methods on the original dataset. The success of the different methods used was compared, and also data augmentation was performed by using SMOTE, KMeansSMOTE, ADASYN, and SVMSMOTE oversampling techniques, which are widely used in the literature to increase the prediction success of the classifiers. For each of eight different machine learning techniques, namely light gradient boosting machine, eXtreme gradient boosting, naive Bayes, decision tree, support vector machine, K-nearest neighbor, random forest, and logistic regression, in addition to classification without data augmentation, a total of 40 classification processes were carried out using four different data augmentation techniques. The results of the classification processes are reported with four different parameters: accuracy, precision, recall, and F1-score. The highest accuracy value of 99.17% was obtained when the light gradient boosting machine classifier was used together with the SMOTE data augmentation technique. It was observed that the measurements obtained in the study were higher than the results obtained in similar studies conducted in the literature in recent years. [ABSTRACT FROM AUTHOR]