4,776 research outputs found
AI Methods in Algorithmic Composition: A Comprehensive Survey
Algorithmic composition is the partial or total automation of the process of music composition
by using computers. Since the 1950s, different computational techniques related to
Artificial Intelligence have been used for algorithmic composition, including grammatical
representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint
programming and evolutionary algorithms. This survey aims to be a comprehensive
account of research on algorithmic composition, presenting a thorough view of the field for
researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project
(IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e InnovaciĂłn, and a grant for
the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo
y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC-
5123) from the ConsejerĂa de InnovaciĂłn y Ciencia de AndalucĂa
A Simple Method to Produce Algorithmic MIDI Music based on Randomness, Simple Probabilities and Multi-Threading
This paper introduces a simple method for producing multichannel MIDI music
that is based on randomness and simple probabilities. One distinctive feature
of the method is that it produces and sends in parallel to the sound card more
than one unsynchronized channels by exploiting the multi-threading capabilities
of general purpose programming languages. As consequence the derived sound
offers a quite ``full" and ``unpredictable" acoustic experience to the
listener. Subsequently the paper reports the results of an evaluation with
users. The results were very surprising: the majority of users responded that
they could tolerate this music in various occasions.Comment: 7 pages, 5 figure
A Planning-based Approach for Music Composition
. Automatic music composition is a fascinating field within computational
creativity. While different Artificial Intelligence techniques have been used
for tackling this task, Planning – an approach for solving complex combinatorial
problems which can count on a large number of high-performance systems and
an expressive language for describing problems – has never been exploited.
In this paper, we propose two different techniques that rely on automated planning
for generating musical structures. The structures are then filled from the bottom
with “raw” musical materials, and turned into melodies. Music experts evaluated
the creative output of the system, acknowledging an overall human-enjoyable
trait of the melodies produced, which showed a solid hierarchical structure and a
strong musical directionality. The techniques proposed not only have high relevance
for the musical domain, but also suggest unexplored ways of using planning
for dealing with non-deterministic creative domains
A Survey of Music Generation in the Context of Interaction
In recent years, machine learning, and in particular generative adversarial
neural networks (GANs) and attention-based neural networks (transformers), have
been successfully used to compose and generate music, both melodies and
polyphonic pieces. Current research focuses foremost on style replication (eg.
generating a Bach-style chorale) or style transfer (eg. classical to jazz)
based on large amounts of recorded or transcribed music, which in turn also
allows for fairly straight-forward "performance" evaluation. However, most of
these models are not suitable for human-machine co-creation through live
interaction, neither is clear, how such models and resulting creations would be
evaluated. This article presents a thorough review of music representation,
feature analysis, heuristic algorithms, statistical and parametric modelling,
and human and automatic evaluation measures, along with a discussion of which
approaches and models seem most suitable for live interaction
A Functional Taxonomy of Music Generation Systems
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 to set the stage for new breakthroughs.Comment: survey, music generation, taxonomy, functional survey, survey,
automatic composition, algorithmic compositio
Computational Creativity and Music Generation Systems: An Introduction to the State of the Art
Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research
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