HIPI: A Hierarchical Performer Identification Model Based on Symbolic Representation of Music

Abstract

Automatic Performer Identification from the symbolic representation of music has been a challenging topic in Music Information Retrieval (MIR).In this study, we apply a Recurrent Neural Network (RNN) model to classify the most likely music performers from their interpretative styles. We study different expressive parameters and investigate how to quantify these parameters for the exceptionally challenging task of performer identification. We encode performerstyle information using a Hierarchical Attention Network (HAN) architecture, based on the notion that traditional western music has a hierarchical structure (note, beat, measure, phrase level etc.). In addition, we present a large-scale dataset consisting of six virtuoso pianists performing the same set of compositions. The experimental results show that our model outperforms the baseline models with an F1-score of 0.845 and demonstrates the significance of the attention mechanism for understanding different performance styles.