Techniques for Generative Melodies Inspired by Music Cognition

Brown, Andrew R.; Gifford, Toby; Davidson, Robert

doi:10.1162/comj_a_00282

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…What is important is to concur that music creators organise sounds according to some criteria. Thus, for computers to create music one needs to endow them with such criteria; to glance over different approaches for doing this, please refer to [7,11,37,39]. One such approach is to program the computer with (a) rules for sequencing musical events and (b) the ability to use those rules.…”

Section: Composing With Transition Rulesmentioning

confidence: 99%

“…The respective amplitude for 001 is equal to 1.0, which means that there is a 100% probability that 000 will be followed by 001 (11). The quantum circuit 7 to compute this transition is rather simple (Figure 10): it should always measure q 2 = 0, q 1 = 0, q 0 = 1, that is 001. In practice, as we are dealing with a small occurrence matrix in this example, here we simply skip the quantum processing altogether, and retrieve the only possible choice classically; see discussion in section 6.…”

Section: Build Quantum Circuit and Measurementioning

confidence: 99%

“…Figure 10: Circuit for the transition 000 =⇒ [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]. 7 In our implementation, we used Qiskit's initialize() function to build the circuit. At this point, the result E t +1 = 001 is appended into the new tune, E becomes equal to 001, and the system moves on to generate the next event.…”

Section: Build Quantum Circuit and Measurementioning

confidence: 99%

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Teaching Qubits to Sing: Mission Impossible?

Miranda¹,

Siegelwax²

2022

Preprint

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This paper introduces QuSing, a system that learns to sing new tunes by listening to examples. QuSing extracts sequencing rules from input music and uses these rules to generate new tunes, which are sung by a vocal synthesiser. We developed a method to represent rules for musical composition as quantum circuits. We claim that such musical rules are quantum native: they are naturally encodable in the amplitudes of quantum states. To evaluate a rule to generate a subsequent event, the system builds the respective quantum circuit dynamically and measures it. After a brief discussion about the vocal synthesis methods that we have been experimenting with, the paper introduces our novel generative music method through a practical example. The paper shows some experiments and concludes with a discussion about harnessing the system's creative potential. Accompanying materials are available in an Appendix. Audio recordings of the musical examples and programming code are available: https://github.com/iccmr-quantum/QuSing.

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Section: Composing With Transition Rulesmentioning

confidence: 99%

Section: Build Quantum Circuit and Measurementioning

confidence: 99%

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Teaching Qubits to Sing: Mission Impossible?

Miranda¹,

Siegelwax²

2022

Preprint

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“…Again, good music-theoretic understanding of the genre made evaluation anduniquely in this literature-resulting specific improvement of the system possible, by means of a version of the Consensual Assessment Technique [Amabile 1996b], discussed below in section 4.2. More recently, Brown et al [2015] have developed a number of melodic generation algorithms based on preference rule and statistical models of music cognition.…”

Section: Melodic and Harmonic Generationmentioning

confidence: 99%

Evaluation of Musical Creativity and Musical Metacreation Systems

2016

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The field of computational creativity, including musical metacreation, strives to develop artificial systems that are capable of demonstrating creative behavior or producing creative artefacts. But the claim of creativity is often assessed, subjectively only on the part of the researcher, and not objectively at all. This paper provides theoretical motivation for more systematic evaluation of musical metacreation and computationally creative systems, and presents an overview of current methods used to assess human and machine creativity that may be adapted for this purpose. In order to highlight the need for a varied set of evaluation tools, a distinction is drawn between three types of creative systems: those which are purely generative; those which contain internal or external feedback; and those which are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and techniques are suggested to help researchers 1) evaluate their systems' creative process and generated artefacts, and test their impact on the perceptual, cognitive, and affective states of the audience, and 2) build mechanisms for reflection into the creative system, including models of human perception and cognition, to endow creative systems with internal evaluative mechanisms to drive self-reflective processes. The first type of evaluation can be considered external to the creative system, and may be employed by the researcher to both better understand the efficacy of their system and its impact, and to incorporate feedback into the system. Here, we take the stance that understanding human creativity can lend insight to computational approaches, and knowledge of how humans perceive creative systems and their output can be incorporated into artificial agents as feedback to provide a sense of how a creation will impact the audience. The second type centers around internal evaluation, in which the system is able to reason about its own behavior and generated output. We argue that creative behavior cannot occur without feedback and reflection by the creative/metacreative system itself. More rigorous empirical testing will allow computational and metacreative systems to become more creative by definition, and can be used to demonstrate the impact and novelty of particular approaches.

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“…The source melodies also provide guidelines for the form over multiple phrases, including the skeleton of pitch height over a melody. The idea of guide melody mean pitches constraining new generation bears a relation to the use of an elastic tendency towards the mean pitch of the phrase within previous psychologically inspired treatments (Brown et al 2015).…”

Section: Melody Generation Algorithmmentioning

confidence: 99%

A Funny Thing Happened on the Way to the Formula: Algorithmic Composition for Musical Theater

Collins

2016

Computer Music Journal

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Allied with the documentary, the resultant musical had a two week West End run in London and was itself broadcast in full; evaluation of the project included both

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Techniques for Generative Melodies Inspired by Music Cognition

Cited by 9 publications

References 17 publications

Teaching Qubits to Sing: Mission Impossible?

Teaching Qubits to Sing: Mission Impossible?

Evaluation of Musical Creativity and Musical Metacreation Systems

A Funny Thing Happened on the Way to the Formula: Algorithmic Composition for Musical Theater

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