A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis
This work addresses a bottleneck for researchers in music informatics and generation by providing tools and data to incorporate SchA, potentially leading to more human-like music analysis and generation, though it is incremental in building infrastructure.
The paper tackled the lack of computer-readable data for Schenkerian Analysis (SchA) in music informatics by introducing a new dataset of over 140 excerpts, a visualization software, and a graph-based representation, enabling deeper integration of SchA into machine learning models.
Schenkerian Analysis (SchA) is a uniquely expressive method of music analysis, combining elements of melody, harmony, counterpoint, and form to describe the hierarchical structure supporting a work of music. However, despite its powerful analytical utility and potential to improve music understanding and generation, SchA has rarely been utilized by the computer music community. This is in large part due to the paucity of available high-quality data in a computer-readable format. With a larger corpus of Schenkerian data, it may be possible to infuse machine learning models with a deeper understanding of musical structure, thus leading to more "human" results. To encourage further research in Schenkerian analysis and its potential benefits for music informatics and generation, this paper presents three main contributions: 1) a new and growing dataset of SchAs, the largest in human- and computer-readable formats to date (>140 excerpts), 2) a novel software for visualization and collection of SchA data, and 3) a novel, flexible representation of SchA as a heterogeneous-edge graph data structure.