arXiv:2510.02746v1 Announce Type: new
Abstract: Music scores are used to precisely store music pieces for transmission and preservation. To represent and manipulate these complex objects, various formats have been tailored for different use cases. While music notation follows specific rules, digital formats usually enforce them leniently. Hence, digital music scores widely vary in quality, due to software and format specificity, conversion issues, and dubious user inputs. Problems range from minor engraving discrepancies to major notation mistakes. Yet, data quality is a major issue when dealing with musical information extraction and retrieval. We present an automated approach to detect notational errors, aiming at precisely localizing defects in scores. We identify two types of errors: i) rhythm/time inconsistencies in the encoding of individual musical elements, and ii) contextual errors, i.e. notation mistakes that break commonly accepted musical rules. We implement the latter using a modular state machine that can be easily extended to include rules representing the usual conventions from the common Western music notation. Finally, we apply this error-detection method to the piano score dataset ASAP. We highlight that around 40% of the scores contain at least one notational error, and manually fix multiple of them to enhance the dataset’s quality.
Expert Commentary: Music Score Quality Improvement Through Automated Error Detection
Music scores have long been used as a means to accurately store and preserve musical compositions. While traditional music notation follows strict rules, the advent of digital formats has introduced new challenges when it comes to ensuring the quality and accuracy of these scores. This article highlights the importance of data quality in the context of musical information extraction and retrieval, emphasizing the need for automated approaches to detect and correct notational errors.
The multidisciplinary nature of this work is evident in the intersection of music theory, computer science, and data analysis. By identifying rhythm/time inconsistencies and contextual errors in music scores, the researchers have developed a modular state machine that can effectively pinpoint deviations from commonly accepted musical conventions. This approach not only enhances the quality of the dataset but also showcases the potential for automated tools to improve the overall integrity of digital music scores.
From a broader perspective, this research contributes to the field of multimedia information systems by demonstrating the application of automated error detection in the realm of music scores. The concepts explored in this study have implications for other areas such as animations, artificial reality, augmented reality, and virtual realities, where precise representation and manipulation of complex objects are essential.
Future Implications and Directions
- Further refinement of automated error detection algorithms to address a wider range of notational errors.
- Exploration of how this approach can be applied to other types of musical scores beyond piano music.
- Integration of machine learning techniques to enhance the accuracy and efficiency of error detection processes.
- Collaboration with experts in music theory and information retrieval to validate the effectiveness of the proposed method.
In conclusion, this study represents a significant step towards improving the quality and reliability of digital music scores through automated error detection. By leveraging interdisciplinary expertise, researchers have demonstrated the potential for innovative solutions that bridge the gap between traditional music notation and modern digital formats.