Finding maximum-length repeating patterns in music databases

Karydis, Ioannis; Νανόπουλος, Αλέξανδρος; Manolopoulos, Yannis

doi:10.1007/s11042-006-0068-5

Cited by 14 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several studies have focused on finding maximal repeating patterns, thus discarding patterns that are subpatterns of a larger pattern that occurs with the same frequency as the sub-patterns (Hsu, Liu, and Chen 1998;Liu, Hsu, and Chen 1999;Karydis, Nanopoulos, and Manolopoulos 2007). The repeating patterns can be searched using encodings more adapted to melody recognition, such as in the stepleap representation used by Cambouropoulos and colleagues (2005).…”

Section: Related Work: Mir and Fugue Analysismentioning

confidence: 99%

Computational Fugue Analysis

Giraud

Groult

Leguy

et al. 2015

Computer Music Journal

View full text Add to dashboard Cite

International audienceOne of the pinnacle forms of classical Western music, the fugue is often used in the teaching of music analysis and composition. Fugues alternate between instances of a subject and other patterns and modulatory sections, which are called episodes. Musicological analyses are generally built on these patterns and sections.We propose several algorithms to perform an automated analysis of a fugue, starting from a score in which all the voices are separated. By focusing on the diatonic similarities between pitch intervals, we detect subjects and countersubjects, as well as partial harmonic sequences inside the episodes. We also propose tools to detect subject scale degrees, cadences and pedals, as well as a method for segmenting the fugue into exposition and episodic parts.Our algorithms were tested on a corpus of 36 fugues by Bach and Shostakovich. We provide formalized ground truth data on this corpus as well as a dynamic visualization of the ground truth and of our computed results. The complete system showed correct or good results for about one half of the fugues tested, enabling us to sketch their design

show abstract

Section: Related Work: Mir and Fugue Analysismentioning

confidence: 99%

Computational Fugue Analysis

Giraud

Groult

Leguy

et al. 2015

Computer Music Journal

View full text Add to dashboard Cite

show abstract

“…Based on features extracted from these histograms the authors of [13] managed a 50% accuracy for 5 genres. Compared to this work, we additionally include the notion of pitch dependency as well as the orientation in repeating patterns, which focus on significant parts of the piece as far as semantics and efficiency are concerned [6,7].…”

Section: Related Workmentioning

confidence: 99%

“…The existence of RPs in musical pieces has been supported by both composers (a starting point for development, which may be repeated possibly in the form of variations) and listeners [2]. As the process of mining musical RPs presents important challenges, several methods, e.g, [6,7], have been proposed in the MIR literature for their extraction.…”

Section: Related Workmentioning

confidence: 99%

Symbolic musical genre classification based on repeating patterns

Karydis

Νανόπουλος

Manolopoulos

2006

Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia

Self Cite

View full text Add to dashboard Cite

This paper presents a genre classification algorithm for symbolic music data. The proposed methodology relies on note pitch and duration features, derived from the repeating patterns and duration histograms of a musical piece, respectively. Note-information histograms have a great capability in capturing a fair amount of information regarding harmonic as well as rhythmic features of different musical genres and pieces, while repeating patterns refer to segments of the piece that are semantically important. Detailed experimental results on intra-classical genres illustrate the significant performance gains due to the proposed features.

show abstract

“…Similarity between a pattern and several parts of a piece may be computed by the Mongeau-Sankoff algorithm [20] and its extensions or by other methods for approximate string matching [7,8], allowing a given number of restricted mismatches. Several studies focus on finding maximal repeating patterns, limiting the search to non-trivial repeating patterns, that is discarding patterns that are a sub-pattern of a larger one with the same frequency [13,14,16,17]. Other studies try to find musically significant themes, with algorithms considering the number of occurrences [25], but also the melodic contour or other features [18].…”

Section: Introductionmentioning

confidence: 99%

Subject and Counter-Subject Detection for Analysis of the Well-Tempered Clavier Fugues

Giraud

Groult

Levé

2013

From Sounds to Music and Emotions

View full text Add to dashboard Cite

Abstract. Fugue analysis is a challenging problem. We propose an algorithm that detects subjects and counter-subjects in a symbolic score where all the voices are separated, determining the precise ends and the occurrence positions of these patterns. The algorithm is based on a diatonic similarity between pitch intervals combined with a strict length matching for all notes, except for the first and the last one. On the 24 fugues of the first book of Bach's Well-Tempered Clavier, the algorithm predicts 66% of the subjects with a musically relevant end, and finally retrieves 85% of the subject occurrences, with almost no false positive.

show abstract

Finding maximum-length repeating patterns in music databases

Cited by 14 publications

References 28 publications

Computational Fugue Analysis

Computational Fugue Analysis

Symbolic musical genre classification based on repeating patterns

Subject and Counter-Subject Detection for Analysis of the Well-Tempered Clavier Fugues

Contact Info

Product

Resources

About