PianoCoRe: Combined and Refined Piano MIDI Dataset
Abstract
Symbolic music datasets with matched scores and performances are essential for many music information retrieval (MIR) tasks. Yet, existing resources often cover a narrow range of composers, lack performance variety, omit note-level alignments, or use inconsistent naming formats. This work presents PianoCoRe, a large-scale piano MIDI dataset that unifies and refines major open-source piano corpora. The dataset contains 250,046 performances of 5,625 pieces written by 483 composers, totaling 21,763 h of performed music. PianoCoRe is released in tiered subsets to support different applications: from large-scale analysis and pre-training (PianoCoRe-C and deduplicated PianoCoRe-B) to expressive performance modeling with note-level score alignment (PianoCoRe-A/A*). The note-aligned subset, PianoCoRe-A, provides the largest open-source collection of 157,207 performances aligned to 1,591 scores to date. In addition to the dataset, the contributions are: (1) a MIDI quality classifier for detecting corrupted and score-like transcriptions and (2) RAScoP, an alignment refinement pipeline that cleans temporal alignment errors and interpolates missing notes. The analysis shows that the refinement reduces temporal noise and eliminates tempo outliers. Moreover, an expressive performance rendering model trained on PianoCoRe demonstrates improved robustness to unseen pieces compared to models trained on raw or smaller datasets. PianoCoRe provides a ready-to-use foundation for the next generation of expressive piano performance research.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the PianoCoRe dataset, a refined and comprehensive MIDI dataset that addresses the limitations of existing resources in symbolic music analysis. This work significantly enhances the foundation for future research in expressive piano performance modeling and music information retrieval, showcasing a meticulous approach to dataset curation and quality assessment.
Methodology
The methodology presented in this paper is robust and comprehensive, detailing a systematic approach to curating and refining a large-scale piano MIDI dataset. The authors employ a multi-tiered strategy that includes deduplication, quality assessment, and note alignment refinement using the RAScoP pipeline. The integration of various existing datasets into a unified collection is particularly noteworthy, as it addresses the inconsistencies and limitations found in previous datasets. The use of a MIDI quality classifier to filter out corrupted transcriptions and the detailed description of the alignment process further enhance the methodological rigor.
Results & evaluation
The experiments conducted demonstrate the effectiveness of the proposed dataset and methodologies. The authors provide a thorough evaluation of the MIDI quality classifier, achieving a high macro F1 score, which indicates the classifier's reliability in distinguishing between performance qualities. Additionally, the application of the dataset in training an expressive performance rendering model shows significant improvements in robustness, suggesting that the dataset effectively supports advanced modeling tasks. However, specific quantitative results from the expressive performance rendering model could further strengthen the experimental validation.
Reproducibility
The paper includes detailed descriptions of the dataset construction process, including data sources, matching methodologies, and quality assessment techniques. The authors provide a GitHub repository link for the project, which enhances reproducibility. However, the paper could benefit from including specific implementation details or code snippets to facilitate replication of the methodologies by other researchers.
Limitations
One limitation identified is the reliance on existing datasets, which may still contain inherent biases or limitations that could affect the quality of the combined dataset. Additionally, while the RAScoP pipeline improves alignment, the paper does not fully address potential edge cases where alignment might still be problematic. The focus on public domain works may also limit the dataset's applicability to contemporary compositions.
Broader impact
The PianoCoRe dataset has the potential to significantly impact the field of music information retrieval and computational musicology by providing a comprehensive resource for training models in expressive performance rendering and analysis. Its tiered structure allows for diverse applications, from large-scale analysis to specific performance modeling tasks, thus fostering advancements in music generation and understanding. The main contribution of this paper is the introduction of the PianoCoRe dataset, a refined and comprehensive MIDI dataset that addresses the limitations of existing resources in symbolic music analysis. This work significantly enhances the foundation for future research in expressive piano performance modeling and music information retrieval, showcasing a meticulous approach to dataset curation and quality assessment.
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