Abstract: Fostered by the introduction of the Music Information Retrieval Evaluation Exchange (MIREX) competition, the number of systems that calculate symbolic melodic similarity has recently increased considerably. To understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help to characterize the systems, highlighting strengths and weaknesses. We also propose a taxonomy that classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited to provide input for new, forthcoming research in the area. The advent of the Internet has made a large quantity of audio and symbolic musical data freely available. The analysis of these data can provide useful insights into several aspects of music. By comparing many musical pieces, it is possible to abstract relevant rules and processes that characterize a particular style. Also, the analysis of large databases can improve our understanding of the generative process, shedding light on the evolutionary path undergone by music over time. To capitalize upon the significant body of knowledge currently stored within online music data sets, a number of reliable and efficient automatic tools have been developed over the last decades. Melodic similarity-detection algorithms are an instance of such tools. When used on online musical data sets, they can provide valuable information on intra-and interwork melodic relationships and on the underlying melodic structures of the pieces analyzed. Given two or more sequences of notes, symbolic melodic similarity (SMS) aims to evaluate their degree of likeness, as human listeners are able to do. This task has relevance both within the academy and in industry. For instance, beyond the purely academic benefits of identifying the degree of likeness between musical pieces and composer practices afforded by melodic similarity systems, the detection of plagiarism constitutes an example of a practical application of this task with clear legal and commercial implications. Many algorithms for judging melodic similarity have been introduced
al. REF provide a relative analysis of existing algorithms that are used to calculate symbolic melodic similarity based on eight criteria.
Symbolic Melodic Similarity: State of the Art and Future Challenges
Computer Music Journal
Computer Music Journal
Computer Science