L-Music: uma abordagem para composição musical assistida usando L-Systems

Generative music systems have been researched for an extended period of time. The scientific corpus of this research field is translating, currently, into the world of the everyday musician and composer. With these tools, the creative process of writing music can be augmented or completely replaced by machines. The work in this document aims to contribute to research in assisted music composition systems. To do so, a review on the state of the art of these fields was performed and we found that a plethora of methodologies and approaches each provide their own interesting results (to name a few, neural networks, statistical models, and formal grammars). We identified Lindenmayer Systems, or L-Systems, as the most interesting and least explored approach to develop an assisted music composition system prototype, aptly named L-Music, due to the ability of producing complex outputs from simple structures. L-Systems were initially proposed as a parallel string rewriting grammar to model algae plant growth. Their applications soon turned graphical (e.g., drawing fractals), and eventually they were applied to music generation. Given that our prototype is assistive, we also took the user interface and user experience design into its well-deserved consideration. Our implemented interface is straightforward, simple to use with a structured visual hierarchy and flow and enables musicians and composers to select their desired instruments; select L-Systems for generating music or create their own custom ones and edit musical parameters (e.g., scale and octave range) to further control the outcome of L-Music, which is musical fragments that a musician or composer can then use in their own works. Three musical interpretations on L-Systems were implemented: a random interpretation, a scale-based interpretation, and a polyphonic interpretation. All three approaches produced interesting musical ideas, which we found to be potentially usable by musicians and composers in their own creative works. Although positive results were obtained, the developed prototype has many improvements for future work. Further musical interpretations can be added, as well as increasing the number of possible musical parameters that a user can edit. We also identified the possibility of giving the user control over what musical meaning L-Systems have as an interesting future challenge.Sistemas de geração de música têm sido alvo de investigação durante períodos alargados de tempo. Recentemente, tem havido esforços em passar o conhecimento adquirido de sistemas de geração de música autónomos e assistidos para as mãos do músico e compositor. Com estas ferramentas, o processo criativo pode ser enaltecido ou completamente substituído por máquinas. O presente trabalho visa contribuir para a investigação de sistemas de composição musical assistida. Para tal, foi efetuado um estudo do estado da arte destas temáticas, sendo que foram encontradas diversas metodologias que ofereciam resultados interessantes de um ponto de vista técnico e musical. Os sistemas de Lindenmayer, ou L-Systems, foram selecionados como a abordagem mais interessante, e menos explorada, para desenvolver um protótipo de um sistema de composição musical assistido com o nome L-Music, devido à sua capacidade de produzirem resultados complexos a partir de estruturas simples. Os L-Systems, inicialmente propostos para modelar o crescimento de plantas de algas, são gramáticas formais, cujo processo de reescrita de strings acontece de forma paralela. As suas aplicações rapidamente evoluíram para interpretações gráficas (p.e., desenhar fractais), e eventualmente também foram aplicados à geração de música. Dada a natureza assistida do protótipo desenvolvido, houve uma especial atenção dada ao design da interface e experiência do utilizador. Esta, é concisa e simples, tendo uma hierarquia visual estruturada para oferecer uma orientação coesa ao utilizador. Neste protótipo, os utilizadores podem selecionar instrumentos; selecionar L-Systems ou criar os seus próprios, e editar parâmetros musicais (p.e., escala e intervalo de oitavas) de forma a gerarem excertos musicais que possam usar nas suas próprias composições. Foram implementadas três interpretações musicais de L-Systems: uma interpretação aleatória, uma interpretação à base de escalas e uma interpretação polifónica. Todas as interpretações produziram resultados musicais interessantes, e provaram ter potencial para serem utilizadas por músicos e compositores nos seus trabalhos criativos. Embora tenham sido alcançados resultados positivos, o protótipo desenvolvido apresenta múltiplas melhorias para trabalho futuro. Entre elas estão, por exemplo, a adição de mais interpretações musicais e a adição de mais parâmetros musicais editáveis pelo utilizador. A possibilidade de um utilizador controlar o significado musical de um L-System também foi identificada como uma proposta futura relevante

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

Evaluation of Musical Creativity and Musical Metacreation Systems

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 article 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 among three types of creative systems: those that are purely generative, those that contain internal or external feedback, and those that 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

INSAM Journal of Contemporary Music, Art and Technology 4 (I/2020)

Human-Machine Collaboration in music, arts, and theory, has come to be a particularly challenging, provocative, and necessary topic. As we underlined in the Call for Papers, the theme of the issue highlighted our intention to give space to overcoming the deep-rooted dichotomy “human vs. machine” in the arts. In finding and understanding the flaws and strengths of both sides through theoretical, inter and trans-disciplinary, and critical thinking, we strive towards creating a platform for open discussions on the subject. In the issue of INSAM Journal in front of us, this conversation is inspired by the propelling artwork of Sougwen Chung

Computational creativity: an interdisciplinary approach to sequential learning and creative generations

Creativity seems mysterious; when we experience a creative spark, it is difficult to explain how we got that idea, and we often recall notions like ``inspiration" and ``intuition" when we try to explain the phenomenon. The fact that we are clueless about how a creative idea manifests itself does not necessarily imply that a scientific explanation cannot exist. We are unaware of how we perform certain tasks, such as biking or language understanding, but we have more and more computational techniques that can replicate and hopefully explain such activities. We should understand that every creative act is a fruit of experience, society, and culture. Nothing comes from nothing. Novel ideas are never utterly new; they stem from representations that are already in mind. Creativity involves establishing new relations between pieces of information we had already: then, the greater the knowledge, the greater the possibility of finding uncommon connections, and the more the potential to be creative. In this vein, a beneficial approach to a better understanding of creativity must include computational or mechanistic accounts of such inner procedures and the formation of the knowledge that enables such connections. That is the aim of Computational Creativity: to develop computational systems for emulating and studying creativity. Hence, this dissertation focuses on these two related research areas: discussing computational mechanisms to generate creative artifacts and describing some implicit cognitive processes that can form the basis for creative thoughts

Towards Greater Computational Modeling in Neurocognitive Creativity Research

Creative cognition is the driving force behind all cultural and scientific progress. In recent years, the field of neurocognitive creativity research (NCR) has made considerable progress in revealing the neural and psychological correlates of creative cognition. However, a detailed understanding of how cognitive processes produce creative ideas, and how these processes interact differently across tasks and individuals, remains elusive. In this article, we argue that the increased adoption of computational modeling can help greatly in achieving this goal. While the verbal theories guiding NCR have evolved from broader accounts into more specific descriptions of neurocognitive processes, they remain more open to interpretation and harder to falsify than formal models. Translating theories into computational models can make them more concrete, accessible, and easier to compare, and helps researchers to develop causal hypotheses for how variation in cognitive factors leads to variation in creative outcomes. Currently, however, computational modeling of creativity is conducted almost entirely separately from NCR, and few attempts have been made to embody the cognitive theories of NCR in models that can simulate performance on common lab-based tasks. In this paper, we discuss theories of creative cognition and how they might benefit from the wider adoption of formal modeling. We also examine recent computational models of creativity and how these might be improved and better integrated with NCR. Finally, we describe a pathway toward a mechanistic understanding of creative cognition through the integration of computational modeling, psychological theory, and empirical research, outlining an example model based on dual-process accounts

Tension-driven Automatic Music Generation

The Ancient Greeks are one of the first civilisations we know of to have created algorithms to compose music. Since then, algorithmic techniques have vastly improved with increasingly sophisticated computers. In the last two decades, much research in this area has focused on two goals: designing algorithms which generate music as close as possible to that of human composers and implementing those algorithms to automatically generate music in interactive scenarios, such as video games. To meet these goals, automatically generated music should: - focus on higher-level concepts, such as musical tension, - have long-term structure, and - be able to adapt to changes in real time. Combining these three requirements is, however, a challenging task. This dissertation investigates three steps to overcome this challenge. First, we argue that Lerdahl’s model of musical tension is suited to the automatic generation of tonal music that has long-term structure and that matches a given tension profile. By means of an illustrative example, we review Lerdhal’s model and implement a novel computational system to automate it. Second, we show that an effective generation strategy is to combine statistical methods with both rule-based methods and generative grammars to create a music generation system. Third, we implement the system and evaluate it through a collection of computational tests and empirical studies. Our evaluation shows that: (1) the system works effectively in real time, as long as the input tension profiles do not contain too many steep transitions, (2) the hierarchical structure perceived by listeners matches the patterns intended by the system in the generated music, and (3) tension-changing input profiles are accurately matched by the generated music

Proceedings of the 19th Sound and Music Computing Conference

Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Étienne (France). https://smc22.grame.f