Generation of Two-Voice Imitative Counterpoint from Statistical Models

Padilla, Victor; Conklin, Darrell

doi:10.9781/ijimai.2018.10.002

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…Gabriel Aguilera et al [21] coded counterpoint rules using a computer algebra system by means of probabilistic logic. Victor Padilla et al [22] developed a system that imitates two-voice Palestrina-style counterpoint. They combined statistical generation and pattern discovery in the counterpoint generation process.…”

Section: Counterpoint Accompanimentmentioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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Engaging computers in composing musical pieces is a challenging and trending field of research. The musical tasks that can be performed or aided by computers' computational powers, are numerous. This paper is concerned with applications of computational intelligence in music composition. Its main objective is to survey various computational intelligence techniques for performing miscellaneous music composition tasks. To achieve this objective, we first define each music composition task, then we discuss the recent applications of each, and the techniques adopted in them. We also highlight the most suitable techniques for performing each task. Our study shows that the most suitable techniques for human composers imitative systems are case-based reasoning and artificial neural networks. It is also shown that Markov models are more suitable for predicting musical notes based on the given previous notes. Genetic algorithms excel in chord progressions generation. Deep neural networks are clever at capturing temporal information of a musical piece. The state-of-the-art generative adversarial networks produce music as close as possible to real compositions. At the end of this study, we shed the light on many future research directions in the field of computer music composition.

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Section: Counterpoint Accompanimentmentioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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“…Among these, rule and constraint-based methods have been explored extensively [6,7], along with grammars [8,9] and statistical methods, from Hidden Markov Models [10,11] to their combinations with pattern-matching models [12]. In a more recent experiment [13], a Markov model is again combined with a pattern template.…”

Section: Related Workmentioning

confidence: 99%

Modeling Baroque Two-Part Counterpoint with Neural Machine Translation

Nichols¹,

Kalonaris²,

Micchi³

et al. 2020

Preprint

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We propose a system for contrapuntal music generation based on a Neural Machine Translation (NMT) paradigm. We consider Baroque counterpoint and are interested in modeling the interaction between any two given parts as a mapping between a given source material and an appropriate target material. Like in translation, the former imposes some constraints on the latter, but doesn't define it completely. We collate and edit a bespoke dataset of Baroque pieces, use it to train an attention-based neural network model, and evaluate the generated output via BLEU score and musicological analysis. We show that our model is able to respond with some idiomatic trademarks, such as imitation and appropriate rhythmic offset, although it falls short of having learned stylistically correct contrapuntal motion (e.g., avoidance of parallel fifths) or stricter imitative rules, such as canon.

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“…To do so, they use a sampling method named Variable Neighborhood Search (VNS), which starts with a randomly generated fragment and optimize it making local changes to increase the probability of the fragment, and they compare it to other sampling methods like random walk or Gibbs sampling. Padilla and Conklin [34] have developed a method to compose Palestrina masses using a combination of statistical models, to capture the stylistic aspects of the music, and pattern discovery to extract the coherence structure of original melodies. The pattern discovery process is performed on a single viewpoint representation of the pieces, and the patterns used to build the coherence structure are used to guide the generation of new musical material.…”

Section: Related Workmentioning

confidence: 99%

Statistics-Based Music Generation Approach Considering Both Rhythm and Melody Coherence

et al. 2019

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This paper presents a music generation method which is an extension of a previously presented method that generates coherent melodies using a melodic coherence structure extracted from a template piece. This extension, which has been applied for generating bertso melodies, adds the generation of the rhythmic content of the melodies, for which a rhythmic coherence structure of the template piece is also created. To do so, a pattern discovery and ranking method is used to discover the rhythmically repeated segments that are interesting, and create a rhythmic coherence structure which can have several levels of nesting. Independent sampling processes have been developed for melodic and rhythmic content, using an adapted optimization method for sampling the rhythmic content of the new pieces. An evaluation process has been carried out to evaluate some of the generated pieces, considering on one hand how the listeners perceive them and on the other hand whether they share the features with bertso melodies. It has been concluded from this evaluation that the method is capable of generating good coherent bertso melodies.

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Generation of Two-Voice Imitative Counterpoint from Statistical Models

Cited by 8 publications

References 23 publications

Applications of Computational Intelligence in Computer Music Composition

Applications of Computational Intelligence in Computer Music Composition

Modeling Baroque Two-Part Counterpoint with Neural Machine Translation

Statistics-Based Music Generation Approach Considering Both Rhythm and Melody Coherence

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