Automatic melody composition based on a probabilistic model of music style and harmonic rules

Roig, Carles; Tardón, Lorenzo J.; Barbancho, Isabel; Barbancho, Ana M.

doi:10.1016/j.knosys.2014.08.018

Cited by 37 publications

(18 citation statements)

References 21 publications

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“…Biles et al proposed an automatic composition system that generates a jazz solo using the genetic algorithm [1]. Roig et al developed an automatic composition system that uses statistical models to model the rhythm patterns and pitch contour [25]. Fukayama et al proposed an automatic composition system Orpheus, which generates melodies based on the prosody of Japanese lyrics [6].…”

Section: Automatic Composition and Melody Completionmentioning

confidence: 99%

A Symbol-level Melody Completion Based on a Convolutional Neural Network with Generative Adversarial Learning

Nakamura

Nose

Chiba

et al. 2020

Journal of Information Processing

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In this paper, we deal with melody completion, a technique which smoothly completes partially-masked melodies. Melody completion can be used to help people compose or arrange pieces of music in several ways, such as editing existing melodies or connecting two other melodies. In recent years, various methods have been proposed for realizing high-quality completion via neural networks. Therefore, in this research, we examine a method of melody completion based on an image completion network. We represent melodies as images and train a completion network to complete those images. The completion network consists of convolution layers and is trained in the framework of generative adversarial networks. We also consider chord progression from musical pieces as conditions. From the experimental result, it was confirmed that the network could generate original melody as a completion result and the quality of the generated melody was not significantly worse than the result of a simple example-based melody completion method.

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Section: Automatic Composition and Melody Completionmentioning

confidence: 99%

A Symbol-level Melody Completion Based on a Convolutional Neural Network with Generative Adversarial Learning

Nakamura

Nose

Chiba

et al. 2020

Journal of Information Processing

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“…An example is the composition system Orpheus [9], which uses an hidden Markov model to generate melodies based on user input, and provides a web-based user interface that has been accessed by several thousand users. Researchers have published many more algorithms for user-driven automatic melody generation, some constrained by abstract parameters [10], others by input melodies in order to generate variations of it [11], to interpolate missing parts [12], or to improvise a fitting countermelody [13].…”

Section: Related Researchmentioning

confidence: 99%

Automatic Music Completion Based on Joint Optimization of Harmony Progression and Voicing

Wilk

Sagayama

2019

Journal of Information Processing

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This paper proposes automatic music completion-the automatic generation of music pieces from any incomplete fragments of music-as a new class of music composition assistance tasks. This is a generalization of conventional music information problems such as automatic melody generation and harmonization. The goal is to turn musical ideas of a user into music pieces, allowing users to quickly explore new ideas and enabling inexperienced users to create their own music. This principle is applicable to a wide variety of music, and as a first step, we present a system that automatically fills in missing parts of a four-part chorale, as well as the underlying harmony progression. The user can input any combination of melody fragments, and freely constrain the harmony. Our system searches for harmonies and melodies that adhere to music-theoretical principles, which requires extensive knowledge and practice for human composers. Accounting for the mutual influence of melodic and harmonic development in music composition, the system is based on a joint model of harmony and voicing. The system was evaluated by analyzing generated music with respect to music theory, in addition to a subjective evaluation experiment. The readers are invited to experiment with our system at http://160.16.202.131/music completion.

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“…A recent study by Roig et al [2014] generates melodies by concatenating rhythmic and melodic patterns sampled from a database. Selection is done based on rules combined with a probabilistic method.…”

Section: Melodymentioning

confidence: 99%

A Functional Taxonomy of Music Generation Systems

2017

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Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys, and facilitates the identification of grand challenges so as to set the stage for new breakthroughs.

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Automatic melody composition based on a probabilistic model of music style and harmonic rules

Cited by 37 publications

References 21 publications

A Symbol-level Melody Completion Based on a Convolutional Neural Network with Generative Adversarial Learning

A Symbol-level Melody Completion Based on a Convolutional Neural Network with Generative Adversarial Learning

Automatic Music Completion Based on Joint Optimization of Harmony Progression and Voicing

A Functional Taxonomy of Music Generation Systems

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