A System for Identifying Common Melodic Phrases in the Masses of Palestrina

Knopke, Ian; Jürgensen, Frauke

doi:10.1080/09298210903288329

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…The previous research work with species counterpoint (mainly first species), and it is not taken into account the complexity of imitations and structure of the counterpoint. Knopke & Jürgersen [43] try to identify common melodic phrases in the masses of Palestrina mapping multi-character music symbols into single-character tokens to build a suffix array structure. This research is focused just on analyses and not in generation.…”

Section: Discussionmentioning

confidence: 99%

“…The use of patterns during generation can help to ensure coherence and intra-opus repetition in generated pieces. A method for the detection of melodic phrases in the masses of Palestrina is described by Knopke & Jurgensen [43], based on the use of suffix arrays to find repeated patterns. Sidorov et al [44] present an approach to music analysis, in which an inferred grammar is explains the structure of a musical work.…”

Section: Generation Using Patternsmentioning

confidence: 99%

“…This interdisciplinary subfield of computer science is growing, and the number of algorithms and researchers in the field highlights its importance. Sequential pattern mining has become an essential data mining task, with broad applications, including market and customer analysis, web log analysis or pattern discovery in protein sequences A survey on sequential pattern mining and the approach of the different algorithms, addressing efficiency and scalability, has been summed up by Khan & Jain [43].…”

Section: B Pattern Discoverymentioning

confidence: 99%

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Generation of Two-Voice Imitative Counterpoint from Statistical Models

Padilla¹,

Conklin²

2018

IJIMAI

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Generating new music based on rules of counterpoint has been deeply studied in music informatics. In this article, we try to go further, exploring a method for generating new music based on the style of Palestrina, based on combining statistical generation and pattern discovery. A template piece is used for pattern discovery, and the patterns are selected and organized according to a probabilistic distribution, using horizontal viewpoints to describe melodic properties of events. Once the template is covered with patterns, two-voice counterpoint in a florid style is generated into those patterns using a first-order Markov model. The template method solves the problem of coherence and imitation never addressed before in previous research in counterpoint music generation. For constructing the Markov model, vertical slices of pitch and rhythm are compiled over a large corpus of dyads from Palestrina masses. The template enforces different restrictions that filter the possible paths through the generation process. A double backtracking algorithm is implemented to handle cases where no solutions are found at some point within a generation path. Results are evaluated by both information content and listener evaluation, and the paper concludes with a proposed relationship between musical quality and information content. Part of this research has been presented at SMC 2016 in Hamburg, Germany.

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Section: Discussionmentioning

confidence: 99%

Section: Generation Using Patternsmentioning

confidence: 99%

Section: B Pattern Discoverymentioning

confidence: 99%

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Generation of Two-Voice Imitative Counterpoint from Statistical Models

Padilla¹,

Conklin²

2018

IJIMAI

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“…There are a number of existing approaches for the discovery and characterization of melodic motifs in diverse contexts, and for different music traditions [13][14][15][16][17][18][19]. A majority of these approaches rely on a symbolic music representation.…”

Section: Introductionmentioning

confidence: 99%

Discovering rāga motifs by characterizing communities in networks of melodic patterns

Gulati

Serrà

Ishwar

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Rāga motifs are the main building blocks of the melodic structures in Indian art music. Therefore, the discovery and characterization of such motifs is fundamental for the computational analysis of this music. We propose an approach for discovering rāga motifs from audio music collections. First, we extract melodic patterns from a collection of 44 hours of audio comprising 160 recordings belonging to 10 rāgas. Next, we characterize these patterns by performing a network analysis, detecting non-overlapping communities, and exploiting the topological properties of the network to determine a similarity threshold. With that, we select a number of motif candidates that are representative of a rāga, the rāga motifs. For a formal evaluation we perform listening tests with 10 professional musicians. The results indicate that, on an average, the selected melodic phrases correspond to rāga motifs with 85% positive ratings. This opens up the possibilities for many musically-meaningful computational tasks in Indian art music, including human-interpretable rāga recognition, semantic-based music discovery, or pedagogical tools.

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“…Most computational approaches for melodies use string methods, treating melodies as sequences of notes or intervals, and modelling distributions and transitions of note properties (Knopke & Jürgensen, 2009;Hillewaere, Manderick, & Conklin, 2009). …”

mentioning

confidence: 99%