Experiments in musical intelligence (EMI): Non‐linear linguistic‐based composition

Cope, David

doi:10.1080/09298218908570541

Cited by 33 publications

(25 citation statements)

References 6 publications

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“…Many different AI techniques have been applied to tasks typically associated with human creativity, including the production of visual and moving image art [17], literature and music [18], but also more broadly in areas such as games [19] and game shows [20].…”

Section: Artificial Creativitymentioning

confidence: 99%

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

McCormack¹,

Hutchings²,

Gifford³

et al. 2020

Org. Sound

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Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.

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Section: Artificial Creativitymentioning

confidence: 99%

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

McCormack¹,

Hutchings²,

Gifford³

et al. 2020

Org. Sound

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“…Conklin [5], focused on trance music, explores a new approach to generating high probability and coherent chord loops from a statistical model trained on a chord sequence corpus. David Cope's Experiments in Musical Intelligence [49] is a system for algorithmic composition heavily based on the conservation of patterns.…”

Section: Generation Using Patternsmentioning

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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“…In algorithmic music composition, the intention and the expectations are far from modest, even though some comprehensive systems, such us Experiments in Musical Intelligence (EMI) developed by Cope (1989), can construct complex and complete pieces. EMI is a project focused on the understanding of musical style and stylistic replication of various composers.…”

Section: First Attemptsmentioning

confidence: 99%

“…Except for the obvious belief that algorithmic composition is an overall cheat, music produced by algorithmic composition is considered somehow inferior, not because it has been produced by an algorithm, but because it belongs to the designer of the algorithm, not the user (Jacob, 1996). Figure 2: Diagram for the general algorithm of EMI (Cope, 1989).…”

Section: Uses Of Algorithmic Compositionmentioning

confidence: 99%

Automatic Melodic Harmonization

Makris

Kayrdis

Sioutas

2016

Advances in Multimedia and Interactive Technologies

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Automatic melodic harmonization tackles the assignment of harmony content (musical chords) over a given melody. Probabilistic approaches to melodic harmonization utilize statistical information derived from a training dataset, producing harmonies that encapsulate some harmonic characteristics of the training dataset. Training data is usually annotated symbolic musical notation. In addition to the obvious musicological interest, different machine learning approaches and algorithms have been proposed for such a task, strengthening thus the challenge of efficient & effective music information utilisation using probabilistic systems. Consequently, the aim of this chapter is to provide an overview of the specific research domain as well as to shed light on the subtasks that have arisen and since evolved. Finally, new trends and future directions are discussed along with the challenges which still remain unsolved.

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Experiments in musical intelligence (EMI): Non‐linear linguistic‐based composition

Cited by 33 publications

References 6 publications

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Generation of Two-Voice Imitative Counterpoint from Statistical Models

Automatic Melodic Harmonization

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