Grammar-based automated music composition in Haskell

Quick, Donya; Hudak, Paul

doi:10.1145/2505341.2505345

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…Following the ideas of Gilbert & Conklin, Groves [29] explores the generation of melodies from a probabilistic analytical model of melodies. Quick & Hudak [30] present a new class of generative grammars called probabilistic temporal graph grammars to handle temporal aspects of music in a way that retains a coherent metrical structure.…”

Section: ) Grammatical Inferencementioning

confidence: 99%

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: ) Grammatical Inferencementioning

confidence: 99%

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Padilla¹,

Conklin²

2018

IJIMAI

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“…A class of generative grammars called Probabilistic Temporal Graph Grammars (PTGG) implemented in Haskell was proposed by [10]. This class aimed at handling the temporal aspect of the music in a way that could retain a coherent metrical structure.…”

Section: Related Researchesmentioning

confidence: 99%

Building Melodic Feature Knowledge of Gamelan Music Using Apriori Based on Functions in Sequence (AFiS) Algorithm

Hastuti

Azhari

Musdholifah

et al. 2016

IRECOS

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Gamelan is a traditional music ensemble from Java, Indonesia, whose melody has characteristics that make the melodic sound of gamelan music easy to recognize. This research aims at building melodic feature knowledge of gamelan music in terms of note sequences rules. The algorithm called AFiS (Apriori based on Functions in Sequence) was also introduced to produce rules by mining the frequent value of note sequences. The basic idea of the AFiS algorithm is to define functions in a sequence, and then to chain the functions based on its position order to identify the support value for each function. The implementation of AFiS algorithm is aimed to define rules of gamelan music melodic feature in terms of ideal note sequences for composition. The evaluation of the accuracy of the note sequences rules is conducted by developing a recommendation system using rules defined in this research. The program is expected to answer correctly to some notes randomly deleted from the sequences. The result shows that the accuracy of the knowledge, and that the note sequences rules of gamelan music based on the correct answer is up to 86.5%. Another evaluation is to find whether the different answers given by the program are accepted as alternative notes to the original notes. This evaluation involved 4 human experts to describe their acceptance of the alternative notes based on the different answers. The result shows that the different notes in 4 of 5 gendings are accepted by the experts as alternative notes.

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“…Because there is not a single best chord sequence to harmonise a melody, the sequence is chosen randomly from a pool of good candidates. Another example comes from Quick and Hudak (2013), who use a generative grammar in which each rule is associated with a probability for generating pieces similar to classical chorales.…”

Section: Related Workmentioning

confidence: 99%

“…This hierarchical nature of melody seems to lend itself well for modelling in a similar nature to our harmony model, possibly with greater room for stochastic processes to model creativity. We hope to include explicit handling of repetition in FCOMP through the use of a model of musical form, similarly to the graph grammars of Quick and Hudak (2013).…”

Section: Handle Repetitionmentioning

confidence: 99%

Functional generation of harmony and melody

Magalhães

Koops

2014

Proceedings of the 2nd ACM SIGPLAN International Workshop on Functional Art, Music, Modeling &Amp; Design

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We present FCOMP, a system for automatic generation of harmony and accompanying melody. Building on previous work on functional modelling of musical harmony, FCOMP first creates a foundational harmony by generating random (but user-guided) values of a datatype that encodes the rules of tonal harmony. Then, a melody that fits to the harmony is generated in a compositional sequence: generate all "possible" melodies, filter them to remove obvious bad choices, pick one candidate note per chord, and then embellish the resulting melodic line.At this very early stage, we aim to define a solid system as a foundation that can be used to further improve upon. We care especially about modularity, so that each individual part of the pipeline can be easily improved, and ease of adaptation, so that users can quickly adapt the generated music to their liking. The resulting system generates simple but harmonious music, and serves as a good case study on how functional programming enables quick and clean prototyping of new ideas, even in the realm of automatic music composition.

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Grammar-based automated music composition in Haskell

Cited by 27 publications

References 20 publications

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Building Melodic Feature Knowledge of Gamelan Music Using Apriori Based on Functions in Sequence (AFiS) Algorithm

Functional generation of harmony and melody

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