Creative improvisation with a reflexive musical bot

Brown, Andrew R.

doi:10.1080/14626268.2017.1419979

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Whilst a plethora of extra-musical communication channels are involved, such as physical gestures, eye contact, and even verbal cues, these are often seen as secondary across jazz [35], free [36], and electroacoustic [37] improvisation genres. Similarly, interactive music systems such as Cypher [38], OSCAR [39], Voyager [40] and CIM [41] privilege this mode of "performance-as-interface" [42] whether or not some additional parametric controls are exposed. Thus human evaluation in the human-machine creative partnership can enter through the creative improvisation itself.…”

Section: Discussionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

“…§1.1). Our own projects have incorporated AI techniques for real-time co-creational applications, including Eden [62], Reflexive Looper [63], and Controlling Interactive Music (CIM) [42].…”

Section: Ai-as-collaboratormentioning

confidence: 99%

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

McCormack¹,

Hutchings²,

Gifford³

et al. 2020

Org. Sound

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“…These form the basis for Machine Musicianship, in which music theory and performance technique are used in the modeling of artificial intelligence behavior (Rowe 2001). Machine Musicianship has since given rise to the study of algorithmic improvisation and musical collaboration with improvisational agents (Fremont 2019;Brown 2018). Artificial intelligence can also shift between the roles of "tool" and an "actor" when used in music creation (Caramiaux and Donnarumma 2020).…”

Section: Introductionmentioning

confidence: 99%

Effects of Deep Neural Networks on the Perceived Creative Autonomy of a Generative Musical System

Smith¹,

Freeman²

2021

AIIDE

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Collaborative AI agents allow for human-computer collaboration in interactive software. In creative spaces such as musical performance, they are able to exhibit creative autonomy through independent actions and decision-making. These systems, called co-creative systems, autonomously control some aspects of the creative process while a human musician manages others. When users perceive a co-creative system to be more autonomous, they may be willing to cede more creative control to it, leading to an experience that users may find more expressive and engaging. This paper describes the design and implementation of a co-creative musical system that captures gestural motion and uses that motion to filter pre-existing audio content. The system hosts two neural network architectures, enabling comparison of their use as a collaborative musical agent. This paper also presents a preliminary study in which subjects recorded short musical performances using this software while alternating between deep and shallow models. The analysis includes a comparison of users' perceptions of the two models' creative roles and the models' impact on the subjects' sense of self-expression.

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What’s that Sound? Culture, Significance and Interpretation of Electronic Sound and Noise

Mills

2019

Tele-Improvisation: Intercultural Interaction in the Online Global Music Jam Session

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Creative improvisation with a reflexive musical bot

Cited by 4 publications

References 14 publications

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence

Effects of Deep Neural Networks on the Perceived Creative Autonomy of a Generative Musical System

What’s that Sound? Culture, Significance and Interpretation of Electronic Sound and Noise

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