Schubert Winterreise Dataset: A Multimodal Scenario for Music Analysis

This article presents a multimodal dataset comprising various representations and annotations of Franz Schubert's song cycle Winterreise. Schubert's seminal work constitutes an outstanding example of the Romantic song cycle - a central genre within Western classical music. Our dataset unifies several public sources and annotations carefully created by music experts, compiled in a comprehensive and consistent way. The multimodal representations comprise the singer's lyrics, sheet music in different machine-readable formats, and audio recordings of nine performances, two of which are freely accessible for research purposes. By means of explicit musical measure positions, we establish a temporal alignment between the different representations, thus enabling a detailed comparison across different performances and modalities. Using these alignments, we provide for the different versions various musicological annotations describing tonal and structural characteristics. This metadata comprises chord annotations in different granularities, local and global annotations of musical keys, and segmentations into structural parts. From a technical perspective, the dataset allows for evaluating algorithmic approaches to tasks such as automated music transcription, cross-modal music alignment, or tonal analysis, and for testing these algorithms' robustness across songs, performances, and modalities. From a musicological perspective, the dataset enables the systematic study of Schubert's musical language and style in Winterreise and the comparison of annotations regarding different annotators and granularities. Beyond the research domain, the data may serve further purposes such as the didactic preparation of Schubert's work and its presentation to a wider public by means of an interactive multimedia experience. With this article, we provide a detailed description of the dataset, indicate its potential for computational music analysis by means of several studies, and point out possibilities for future research.