Region Prediction from Hungarian Folk Music Using Convolutional Neural Networks

摘要

Early 20th century research on folk music and its connection to regional cultures has revealed potential clues for understanding the dynamics and organization of communities over history. Therefore, significant effort has been allocated to collecting and organizing folk music into databases both in written and recorded form. Recent years have provided great advances in the fields of data analysis and machine learning, prompting musicologists to apply these advanced statistical methods to analyze the musical remnants. The present work studies how supervised machine learning methods can be applied to analyze folk music: we train different convolutional neural network classifiers-time-content, frequency-content, black-box, convolutional recurrent neural network and time-frequency architectures- to predict folkloric regions. Results suggest that Transylvanian folkloric regions are distinguishable by the rhythmic content of their music, and while nearby villages have a higher probability of having their predicted labels swapped, the two most often confused regions are geographically remote areas having historically motivated similarity.