Evaluating cognitive models of musical composition
Anthony Wiggins2007, Proceedings of the 4th international joint workshop on computational creativity
Sign up for access to the world's latest research
checkGet notified about relevant papers
checkSave papers to use in your research
checkJoin the discussion with peers
checkTrack your impact
Abstract
We present a method for the evaluation of creative systems. We deploy a learning-based perceptual model of musical melodic listening in the generation of tonal melodies and evaluate its output quantitatively and objectively, using human judges. Then we show how the system can be enhanced by the application of mathematical methods over data supplied by the judges. The outcome to some extent addresses the criticisms of the experts. We suggest that this is a first step on the road to autonomously learning, introspective, creative ...
Related papers
Techniques for Generative Melodies Inspired by Music Cognition
Computer Music Journal, 2015
This paper presents a series of algorithmic techniques for melody generation, inspired by models of music cognition. The techniques are designed for interactive composition — and so privilege brevity, simplicity and flexibility over fidelity to the underlying models. The cognitive models canvassed span gestalt, preference rule, and statistical learning perspectives; a diverse collection with a common thread—the centrality of ‘expectations’ to music cognition. We operationalize some recurrent themes across this collection as probabilistic descriptions of melodic tendency, codifying them as stochastic melody generation techniques. The techniques are combined into a concise melody generator, with salient parameters exposed for ready manipulation in real time. These techniques may be especially relevant to algorithmic composers, the live coding community, and to music psychologists/theorists interested in how computational interpretations of cognitive models ‘sound’ in practice.
Artificial intelligence methods for music generation: a review and future perspectives
Nature-Inspired Computation and Swarm Intelligence, 2020
Assessing Musical Similarity for Computational Music Creativity
Journal of the Audio Engineering Society, 2018
Computationally creative systems require semantic information when reflecting or self reasoning on their output. In this paper we outline the design of a computationally creative musical performance system aimed at producing virtuosic interpretations of musical pieces and provide an overview of its implementation. The case-based reasoning part of the system relies on a measure of musical similarity based on the FANTASTIC and SynPy toolkits that provide melodic and syncopated rhythmic features, respectively. We conducted a listening test based on pair-wise comparison to assess to what extent the machine-based similarity models match human perception. We found the machine-based models to differ significantly to human responses due to differences in participants' responses. The best performing model relied on features from the FANTASTIC toolkit obtaining a rank match rate with human response of 63%, while features from the SynPy toolkit only obtained a ranking match rate of 46%. While more work is needed on a stronger model of similarity, we do not believe these results prevent FANTASTIC features being used as a measure of similarity within creative systems. 2 CASE-BASED REASONING WITHIN MUSICAL PERFORMANCE SYSTEMS 2.1 Case-Based Reasoning All performing musicians use their own previous experiences, knowledge, and ability to develop a musical
Generative Music, Cognitive Modelling, and Computer-Assisted Composition in MusiCog and ManuScore
Music composition is a complex, multi-modal human activity, engaging faculties of percep- tion, memory, motor control, and cognition, and drawing on skills in abstract reasoning, problem solving, creativity, and aesthetic evaluation. For centuries musicians, theorists, mathematicians—and more recently computer scientists—have attempted to systematize composition, proposing various formal methods for combining sounds (or symbols repre- senting sounds) into structures that might be considered musical. Many of these systems are grounded in the statistical modelling of existing music, or in the mathematical formal- ization of the underlying rules of music theory. This thesis presents a different approach, looking at music as a holistic phenomenon, arising from the integration of perceptual and cognitive capacities. The central contribution of this research is an integrated cognitive architecture (ICA) for symbolic music learning and generation called MusiCog. Inspired by previous ICAs, ...
Automatic Melody Composition and Evolution: A Cognitive-based Approach
Proceedings of the 9th Conference on Interdisciplinary Musicology, 2014
Music composition is a highly interdisciplinary process.To understand it deeply, a number of approaches have been used from different fields, such as musicology, music theory, music cognition and philosophy. During recent decades, numerous techniques based on Artificial Intelligence (AI) have been proposed. In particular, many AI systems focus on automatic melodic composition. Most of these systems try to generate melodies enjoyable by a human, but they completely ignore the way in which humans actually compose. Humans create music by exploiting a mixed top-down bottom-up approach,characterised by high-level cognition processes and rules.In this paper, we propose a memetic model for music composition,which considers both psychological and social levels. The former level analyses the actual cognitive mechanisms and procedures involved while composing music: namely, museme network, compositional grammar and evaluation module. The social level puts the figure of the composer into perspective within her musical environment. The introduced memetic model is encoded in a two-step algorithm. Firstly,a top-down approach is used for defining the overall structure of a melody. Secondly, the given structure is filled with musical content,following a bottom-up strategy, that fosters emergent behaviour. The proposed algorithm is the first system we are aware of which can evolve its own compositional style. Stylistic change is achieved by modifying grammar rules and the museme network. Finally, the paper provides an analysis of generated melodies.
Aspects of a Cognitive Theory of Creativity In Musical Composition
Proceedings of the ECAI02 Workshop on …, 2002
Review of CSMC16: What is Computational “Creativity” and How can it be Evaluated in the Context of Music?
Journal of Creative Music Systems, 2017
The 1st Conference on Computer Simulation of Musical Creativity (CSMC16) was held June 17–19, 2016 at the University of Huddersfield. Several themes emerged in the conference discussions addressing some of the fundamental questions of what “creativity” is, or could be, as well as issues regarding methodologies for evaluating the potential “creativity” of computational systems. For this conference review I invoke Wiggins’s (2006) formalisation of creative systems, understood as searches in conceptual spaces, and I use this as a working understanding of creativity in order to suggest some questions related to the papers and discussions; these questions may provide fuel for themes in future conferences in this cross-disciplinary field.
Evaluating Computational Creativity: A Standardised Procedure for Evaluating Creative Systems and its Application
Summary This thesis proposes SPECS: a Standardised Procedure for Evaluating Creative Systems. No methodology has been accepted as standard for evaluating the creativity of a system in the field of computational creativity and the multi-faceted and subjective nature of creativity generates substantial definitional issues. Evaluative practice has developed a general lack of rigour and systematicity, hindering research progress. SPECS is a standardised and systematic methodology for evaluating computational creativity. It is flexible enough to be applied to a variety of different types of creative system and adaptable to specific demands in different types of creativity. Researchers are required to be specific about what creativity entails in the domain they work in and what standards they test a system’s creativity by. To assist researchers, definitional issues are investigated and a set of components representing aspects of creativity is presented, which was empirically derived using computational linguistics analysis. These components are offered as a general definition of creativity that can be customised to account for any specific priorities for creativity in a given domain. SPECS is applied in a case study for detailed comparisons of the creativity of four musical improvisation systems, identifying which systems are more creative than others and why. In a second case study, SPECS is used to capture initial impressions on the creativity of systems presented at a 2011 computational creativity research event. Five systems performing different creative tasks are compared and contrasted. These case studies exemplify the valuable information that can be obtained on a system’s strengths and weaknesses. SPECS gives researchers vital feedback for improving their systems’ creativity, informing further progress in computational creativity research.""
Music Generation System Based on a Human Instinctive Creativity
2018
Most human beings, not only composers, have musical creativity; they can hum and whistle without musical education. We focused on the creativity of creating simple melodies such as humming, and developed a system that generated three melodies based on the physical relationship of notes and probability density functions. We con(cid:12)rmed that the system could create simple melodies like humming and whistling. Moreover, we con(cid:12)rmed that the output melodies of the system included various musical elements such as mode, scale, and rhythm.
Related topics
Related papers
Creativity in machines: Music composition using artificial intelligence
ASIAN JOURNAL OF CONVERGENCE IN TECHNOLOGY, 2020
In this paper we propose a framework to take the next step towards making creative machines. Taking cue from Turing's Mind Paper (1950) to more recent studies by Riedl in ''The Lovelace 2.0 test of artificial creativity and intelligence' we try to examine a very creative area of human creativitymusic. We have summarized the different works published on artificial intelligence and machine learning implemented for algorithmic music composition. Comparison of different algorithms-techniques including key features, advantages, disadvantages, common issues, trade-off and future aspects are discussed in detail. We then propose our own framework of how machines can be made to learn creativity.
Computer models of musical creativity: a review of computer models of musical creativity by David Cope
Literary and linguistic computing, 2008
A Generate-And-Sense Approach to Automated Music Composition
International Journal on Artificial Intelligence Tools, 2005
Nobody would deny that music may evoke deep and profound emotions. In this paper, we present a perceptual music composition system that aims at the controlled manipulation of a user's emotional state. In contrast to traditional composing techniques, the single components of a composition, such as melody, harmony, rhythm and instrumentation, are selected and combined in a user-specific manner without requiring the user to continuously provide comments on the music employing input devices, such as keyboard or mouse.
Editorial: JCMS Special Issue of the first Conference on AI Music Creativity
Journal of Creative Music Systems
The International conference on AI Music Creativity (AIMC, https://aimusiccreativity.org/) is the merger of the international workshop on Musical Metacreation MUME (https://musicalmetacreation.org/) and the conference series on Computer Simulation of Music Creativity (CSMC, https://csmc2018.wordpress.com/). This special issue gathers selected papers from the first edition of the conference along with paper versions of two of its keynotes.This special issue contains six papers that apply novel approaches to the generation and classification of music. Covering several generative musical tasks such as composition, rhythm generation, orchestration, as well as some machine listening task of tempo and genre recognition, these selected papers present state of the art techniques in Music AI. The issue opens up with an ode on computer Musicking, by keynote speaker Alice Eldridge, and Johan Sundberg's use of analysis-by-synthesis for musical applications.
Towards a Framework for the Evaluation of Machine Compositions
Proceedings of the AISB'01 Symposium on AI and …, 2001
Intelligent Music Composition
Swarm Intelligence and Bio-Inspired Computation, 2013
Automatic music composition has blossomed with the introduction of intelligent methodologies in Computer Science. Thereby, many methodologies for automatic music composition have been, or could be described as "intelligent", but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what intelligent music composition methodologies are, through proposing and analyzing a detailed categorization of them. Towards this perspective, methodologies that incorporate bio-inspired intelligent algo-1 rithms (such as Cellular Automata, L-systems, Genetic Algorithms and Swarm Intelligence among others), as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the "unsupervised", the "supervised" and the "interactive" intelligent music composition methodologies.
Artificial Computational Creativity based on Collaborative Intelligence in Music
AImc 2021, 2021
In this paper, I will propose a series of Artificial Computer Creativity (ACC) techniques based on Collaborative Intelligence from a multidisciplinary approach. The common thread here are some reflections on the Turing Test (TT) that will inspire alternative metrics of validation. I will propose Collaborative Intelligence (CI) techniques as an expansion of anthropocentric ACC by: replacing the idea of imitation in its basis with playing a game, using selfreferentiality and circularity between the generative and the validation processes; having hybrid man-machine networks; incorporating algorithms that function as mediators of the nodes in hybrid networks avoiding centralities and by integrating self-referential metrics in the works themselves. Finally, I will show how these techniques have been used in a set of works.
A Music-generating System Inspired by the Honing Theory of Creativity
Palo Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press. , 2016
This paper presents NetWorks (NW), a music-generation system that uses a hierarchically clustered scale-free network to generate music that ranges from orderly to chaotic. NW was inspired by the Honing Theory of creativity, according to which human-like creativity hinges on the capacity to (1) maintain dynamics at the 'edge of chaos' through self-organization aimed at minimizing 'psychological entropy', and (2) shift between analytic and associative processing modes. At the edge of chaos, NW generates patterns that exhibit emergent complexity through coherent development at low, mid, and high levels of musical organization, and often suggests goal-seeking behavior. The Core consists of four 16-node modules: one each for pitch, velocity, duration, and entry delay. The Core allows users to define how nodes are connected, and rules that determine when and how nodes respond to their inputs. The Mapping Layer allows users to map node output values to MIDI data that is routed to software instruments in a digital audio workstation. The scale-free architecture of NW's analytic and associative processing modes allows information to flow both bottom-up (from non-hub nodes to hubs) and top-down (from hubs to non-hub nodes). Introduction This paper presents NetWorks (NW), a music-generating program inspired by the view that (1) the human mind is a complex adaptive system (CAS), and thus (2) human-like computational creativity can be achieved by drawing on the science of complex systems. NW uses scale-free networks and 'edge of chaos' dynamics to generate music that is engaging and aesthetically pleasing. The approach dates back to a CD of emergent, self-organizing computer music based on cellular automata and asynchronous genetic networks titled "Voices From The Edge of Chaos" (Bell 1998), and more generally to the application of artificial life models to computer assisted composition, generative music, and sound synthesis (Beyls Todd and Loy 1991). First, we summarize key elements of a CAS-inspired theory of creativity. Next, we outline the architecture of NW, evaluate its outputs, and highlight some of its achievements. Finally, we summarize how NW adheres to principles of honing theory and CAS, and how this contributes to the appealing musicality of its output.
Musical creativity between symbolic modelling and perceptual constraints
Musical creativity: multidisciplinary research in …, 2006
This paper addresses the topic of musical creativity and its underlying mechanisms. An operational definition is provided in an attempt to bring together in a theoretical framework such distinct musical behaviours as listening, performing and composing. Based on the concepts of adaptation and the epistemic control system, it is possible to conceive of the music user as an adaptive device responding to the sonic environment. In order to do so he or she can make new distinctions, perform internal computations on the observables and create different semantic relations with the sonic world. As such it is possible to go beyond perceptual constraints and to conceive of coping with music in terms of creative thinking and epistemic autonomy. A central thesis in this approach is that creativity occurs at the epistemic levels of musical input and output as well as at the computational level.
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.