Algorithms For Computing Approximate Repetitions In Musical Sequences

Cambouropoulos, Emilios; Crochemore, Maxime; Iliopoulos, Costas S.; Mouchard, Laurent; Pinzón, Yoan J.

doi:10.1080/00207160213939

Cited by 51 publications

(40 citation statements)

References 13 publications

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“…It may be useful to allow some tolerance, e.g., ±1 semitone, to account for matches of patterns, for instance, in relative or parallel keys. This would be a kind of δ-approximate matching [4].…”

Section: Resultsmentioning

confidence: 99%

“…An obvious way is to extend matching to include duration consolidation/fragmentation (when a pitch interval is fragmented/ consolidated so are the rhythmic durations fragmented/consolidated). Another way is to add extra constraints such as an overall number of fragmentations/consolidations allowed per query (similar to γ-approximate matching where a threshold for a valid match is defined as the sum of the differences over the entire match [4]) Secondly, the current implementation allows only one-to-many and many-to-one matches (that is one interval consolidated/fragmented to many intervals). This way it is unlikely that two ornamented versions of the same underlying melody can be matched (e.g.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Melodic String Matching via Interval Consolidation and Fragmentation

Barton

Cambouropoulos

Iliopoulos

et al. 2012

IFIP Advances in Information and Communication Technology

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Abstract. In this paper, we address the problem of melodic string matching that enables identification of varied (ornamented) instances of a given melodic pattern. To this aim, a new set of edit distance operations adequate for pitch interval strings is introduced. Insertion, deletion and replacement operations are abolished as irrelevant. Consolidation and fragmentation are retained, but adapted to the pitch interval domain, i.e., two or more intervals of one string may be matched to an interval from a second string through consolidation or fragmentation. The melodic interval string matching problem consists of finding all occurrences of a given pattern in a melodic sequence that takes into account exact matches, consolidations and fragmentations of intervals in both the sequence and the pattern. We show some properties of the problem and an algorithm that solves this problem is proposed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Melodic String Matching via Interval Consolidation and Fragmentation

Barton

Cambouropoulos

Iliopoulos

et al. 2012

IFIP Advances in Information and Communication Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Current approaches follow two different strategies. One is based on numerical similarity (Figure 2, top): patterns are identified when dissimilarity does not exceed a given threshold (Cope, 1991;Rolland, 1999;Cambouropoulos et al, 2002). This threshold cannot however be defined precisely, and is therefore tuned arbitrarily by the user, which questions the general relevance of the results based on this strategy.…”

Section: An Adaptive Pattern Identificationmentioning

confidence: 99%

Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering

Lartillot

2005

Journal of New Music Research

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We present a computational model for discovering repeated patterns in symbolic representations of monodic music. Patterns are discovered through an incremental adaptive identification along a multi-dimensional parametric space. The difficulties of pattern discovery mainly come from combinatorial redundancies, that our model is able to control efficiently. A specificity relation is defined between pattern descriptions, unifying suffix and inclusion relations and enabling a filtering of redundant descriptions. Combinatorial proliferation caused by successive repetitions of patterns is managed using cyclic patterns. The modelling of these redundancy control mechanisms enables an automation of musicologyrelevant analyses of musical databases.

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“…-length constraint -the total length of the notes {y b−k+1 ...y b }, with their associated rests, is exactly the length of x a ; -pitch constraint -at least one of the pitches y b−k+1 ...y b must be equal to the pitch of x a , regardless of the octave.To match minor variations, we simply use a "diatonic equivalence", considering as equal pitches differing from only one chromatic semitone (or, when the pitch spelling is not known, allowing ±1 semitone between the sequences, as in the δ-approximation [5,24]). …”

Section: A Fragmentation Operation For Variation Matchingmentioning

confidence: 99%

Fragmentations with Pitch, Rhythm and Parallelism Constraints for Variation Matching

Giraud

Déguernel

Cambouropoulos

2014

Lecture Notes in Computer Science

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Abstract.Composers commonly employ ornamentation and elaboration techniques to generate varied versions of an initial core melodic idea. Dynamic programming techniques, based on edit operations, are used to find similarities between melodic strings. However, replacements, insertions and deletions may give non-musically pertinent similarities, especially if rhythmic or metrical structure is not considered. We propose, herein, to compute the similarity between a reduced query and a melody employing only fragmentation operations. Such fragmentations transform one note from the reduced query into a possible large set of notes, taking into account pitch and rhythm constraints, as well as elementary parallelism information. We test the proposed algorithm on four "theme and variations" piano pieces by W. A. Mozart and L. van Beethoven and show that the proposed constrained fragmentation operations are capable of detecting simple variations with high sensitivity and specificity.

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Algorithms For Computing Approximate Repetitions In Musical Sequences

Cited by 51 publications

References 13 publications

Melodic String Matching via Interval Consolidation and Fragmentation

Melodic String Matching via Interval Consolidation and Fragmentation

Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering

Fragmentations with Pitch, Rhythm and Parallelism Constraints for Variation Matching

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