Constructing proximity graphs to explore similarities in large-scale melodic datasets

This paper investigates the construction of proximity graphs in order to allow users to explore similarities in melodic datasets. A key part of this investigation is the use of a multilevel framework for measuring similarity in symbolic musical representations. The basis of the framework is straightforward: initially each tune is normalised and then recursively coarsened, typically by removing weaker off-beats, until the tune is reduced to a skeleton representation with just one note per bar. Melodic matching can then take place at every level: the multilevel matching implemented here uses recursive variants of local alignment algorithms, but in principle a variety of similarity measures could be used. The multilevel framework is also exploited with the use of early termination heuristics at coarser levels, both to reduce computational complexity and, potentially, to enhance the matching qualitatively. The results of the matching algorithm are then used to construct proximity graphs which are displayed as part of an online interface for users to explore melodic similarities within a corpus of tunes

What does the Mongeau-Sankoff algorithm compute?

How similar are two melodies? Proposed in 1990, the Mongeau-Sankoff algorithm computes the best alignment between two melodies with insertion, deletion, substitution , fragmentation, and consolidation operations. This popular algorithm is sometimes misunderstood. Indeed, computing the best edit distance, which is the best chain of operations, is a more elaborated problem. Our objective is to clarify the usage of the Mongeau-Sankoff algorithm. In particular, we observe that an alignment is a restricted case of edition. This is especially the case when some edit operations overlap, e.g. when one further changes one or several notes resulting of a fragmentation or a consolidation. We propose recommendations for people wanting to use or extend this algorithm, and discuss the design of combined or extended operations, with specific costs

A Comparison of Symbolic Similarity Measures for Finding Occurrences of Melodic Segments

To find occurrences of melodic segments, such as themes, phrases and motifs, in musical works, a well-performing similarity measure is needed to support human analysis of large music corpora. We evaluate the performance of a range of melodic similarity measures to find occurrences of phrases in folk song melodies. We compare the similarity measures correlation distance, city-block distance, Euclidean distance and alignment, proposed for melody comparison in computational ethnomusicology; furthermore Implication-Realization structure alignment and B-spline alignment, forming successful approaches in symbolic melodic similarity; moreover, wavelet transform and the geometric approach Structure Induction, having performed well in musical pattern discovery. We evaluate the success of the different similarity measures through observing retrieval success in relation to human annotations. Our results show that local alignment and SIAM perform on an almost equal level to human annotators

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin