BPSD: A Coherent Multi-Version Dataset for Analyzing the First Movements of Beethoven's Piano Sonatas

Abstract

This paper introduces the Beethoven Piano Sonata Dataset (BPSD), a multi-version dataset focusing on the first movements of Beethoven's 32 piano sonatas. Recognized as pivotal works in classical music, Beethoven's piano sonatas have profoundly shaped Western classical music, holding a significant place in cultural history. The BPSD includes sheet music in different machine-readable formats and audio recordings from 11 performances, with 4 of them being in the public domain and freely accessible for research purposes. A key feature of BPSD is its coherence, ensuring alignment of all versions on a unified musical timeline and enforcing consistent structures through careful editing of both score and audio representations. The focus and main motivation for the design choices made in BPSD are on the technical and computational level. In particular, BPSD facilitates the assessment of algorithmic approaches in tasks like harmony analysis, structure analysis, music transcription, beat and downbeat estimation, and score following. The dataset's coherence makes it an ideal platform for systematically training and evaluating deep learning methods, shedding light on their robustness and uncovering data biases across different data splits using cross-version strategies for evaluation. To ease applicability for computational approaches, the BPSD is based on various simplifications that may be disputable from a musicological perspective. Rather than providing novel musicological annotations, the main conceptual contribution of BPSD with its measure annotations is to provide a framework for transferring existing annotations from the symbolic to the audio domain. We hope that, as such, BPSD is also useful for the systematic analysis and exploration of Beethoven's piano sonatas, providing insights into their influence on the development of harmony and structure in Western classical music. Beyond research applications, the dataset also holds educational potential, aiding in the preparation and presentation of Beethoven's work to a broader audience through interactive multimedia experiences. This paper delivers a comprehensive overview of the BPSD, highlighting its potential for computational musicology and outlining future research directions.

Authors

• Johannes Zeitler
• Ch. Weiß
• Vlora Arifi-Müller
• Meinard Müller