172 resultados para generative music
Resumo:
This workshop focuses upon research about the qualities of community in music and of music in community facilitated by technologically supported relationships. Generative media systems present an opportunity for users to leverage computational systems to form new relationships through interactive and collaborative experiences. Generative music and art are a relatively new phenomenon that use procedural invention as a creative technique to produce music and visual media. Early systems have demonstrated the potential to provide access to collaborative ensemble experiences for users with little formal musical or artistic expertise. This workshop examines the relational affordances of these systems evidenced by selected field data drawn from the Network Jamming Project. These generative performance systems enable access to unique ensembles with very little musical knowledge or skill and offer the possibility of interactive relationships with artists and musical knowledge through collaborative performance. In this workshop we will focus on data that highlights how these simulated experiences might lead to understandings that may be of social benefit. Conference participants will be invited to jam in real time using virtual interfaces and to evaluate purposively selected video artifacts that demonstrate different kinds of interactive relationship with artists, peers, and community and that enrich the sense of expressive self. Theoretical insights about meaningful engagement drawn from the longitudinal and cross cultural experiences will underpin the discussion and practical presentation.
Resumo:
The use of Cellular Automata (CA) for musical purposes has a rich history. In general the mapping of CA states to note-level music representations has focused on pitch mapping and downplayed rhythm. This paper reports experiments in the application of one-dimensional cellular automata to the generation and evolution of rhythmic patterns. A selection of CA tendencies are identified that can be used as compositional tools to control the rhythmic coherence of monophonic passages and the polyphonic texture of musical works in broad-brush, rather than precisely deterministic, ways. This will provide the composer and researcher with a clearer understanding of the useful application of CAs for generative music.
Resumo:
In this article we identify how computational automation achieved through programming has enabled a new class of music technologies with generative music capabilities. These generative systems can have a degree of music making autonomy that impacts on our relationships with them; we suggest that this coincides with a shift in the music-equipment relationship from tool use to a partnership. This partnership relationship can occur when we use technologies that display qualities of agency. It raises questions about the kinds of skills and knowledge that are necessary to interact musically in such a partnership. These are qualities of musicianship we call eBility. In this paper we seek to define what eBility might consist of and how consideration of it might effect music education practice. The 'e' in eBility refers not only to the electronic nature of computing systems but also to the ethical, enabling, experiential and educational dimensions of the creative relationship with technologies with agency. We hope to initiate a discussion around differentiating what we term representational technologies from those with agency and begin to uncover the implications of these ideas for music educators in schools and communities. We hope also to elucidate the emergent theory and practice that has enabled the development of strategies for optimising this kind of eBility where the tool becomes partner. The identification of musical technologies with agency adds to the authors’ list of metaphors for technology use in music education that previously included tool, medium and instrument. We illustrate these ideas with examples and with data from our work with the jam2jam interactive music system. In this discussion we will outline our experiences with jam2jam as an example of a technology with agency and describe the aspects of eBility that interaction with it promotes.
Resumo:
For some time we have jokingly referred to our network jamming research with jam2jam as ‘Switched on Orff’ (Brown, Sorensen and Dillon 2002; Dillon 2003; Dillon 2006; Dillon 2006; Brown and Dillon 2007). The connection with electronic music and Wendy Carlos’ classic work ‘Switched on Bach’ was obvious; we were using electronic music in schools and with children. The deeper connection with Orff however was about recognising that electronic music and instruments could have cultural values and knowledge embedded in their design and practice in same way as what has come to be known as the Orff method (Orff and Keetman 1958-66). However whilst the Orff method focuses upon Western art music perceptual framework electronic instruments have the potential to have more fluid musical environments and even to move to interdisciplinary study by including visual media. Whilst the Orff method focused on making sense of Western art music through experience electronic environments potentially can make sense of the world of multi media that pervades our lives.
Resumo:
This paper describes algorithms that can musically augment the realtime performance of electronic dance music by generating new musical material by morphing. Note sequence morphing involves the algorithmic generation of music that smoothly transitions between two existing musical segments. The potential of musical morphing in electronic dance music is outlined and previous research is summarised; including discussions of relevant music theoretic and algorithmic concepts. An outline and explanation is provided of a novel Markov morphing process that uses similarity measures to construct transition matrices. The paper reports on a ‘focus-concert’ study used to evaluate this morphing algorithm and to compare its output with performances from a professional DJ. Discussions of this trial include reflections on some of the aesthetic characteristics of note sequence morphing. The research suggests that the proposed morphing technique could be effectively used in some electronic dance music contexts.
Resumo:
This project investigates machine listening and improvisation in interactive music systems with the goal of improvising musically appropriate accompaniment to an audio stream in real-time. The input audio may be from a live musical ensemble, or playback of a recording for use by a DJ. I present a collection of robust techniques for machine listening in the context of Western popular dance music genres, and strategies of improvisation to allow for intuitive and musically salient interaction in live performance. The findings are embodied in a computational agent – the Jambot – capable of real-time musical improvisation in an ensemble setting. Conceptually the agent’s functionality is split into three domains: reception, analysis and generation. The project has resulted in novel techniques for addressing a range of issues in each of these domains. In the reception domain I present a novel suite of onset detection algorithms for real-time detection and classification of percussive onsets. This suite achieves reasonable discrimination between the kick, snare and hi-hat attacks of a standard drum-kit, with sufficiently low-latency to allow perceptually simultaneous triggering of accompaniment notes. The onset detection algorithms are designed to operate in the context of complex polyphonic audio. In the analysis domain I present novel beat-tracking and metre-induction algorithms that operate in real-time and are responsive to change in a live setting. I also present a novel analytic model of rhythm, based on musically salient features. This model informs the generation process, affording intuitive parametric control and allowing for the creation of a broad range of interesting rhythms. In the generation domain I present a novel improvisatory architecture drawing on theories of music perception, which provides a mechanism for the real-time generation of complementary accompaniment in an ensemble setting. All of these innovations have been combined into a computational agent – the Jambot, which is capable of producing improvised percussive musical accompaniment to an audio stream in real-time. I situate the architectural philosophy of the Jambot within contemporary debate regarding the nature of cognition and artificial intelligence, and argue for an approach to algorithmic improvisation that privileges the minimisation of cognitive dissonance in human-computer interaction. This thesis contains extensive written discussions of the Jambot and its component algorithms, along with some comparative analyses of aspects of its operation and aesthetic evaluations of its output. The accompanying CD contains the Jambot software, along with video documentation of experiments and performances conducted during the project.
Resumo:
We advocate for the use of predictive techniques in interactive computer music systems. We suggest that the inclusion of prediction can assist in the design of proactive rather than reactive computational performance partners. We summarize the significant role prediction plays in human musical decisions, and the only modest use of prediction in interactive music systems to date. After describing how we are working toward employing predictive processes in our own metacreation software we reflect on future extensions to these approaches.
Resumo:
Previous work by Professor John Frazer on Evolutionary Architecture provides a basis for the development of a system evolving architectural envelopes in a generic and abstract manner. Recent research by the authors has focused on the implementation of a virtual environment for the automatic generation and exploration of complex forms and architectural envelopes based on solid modelling techniques and the integration of evolutionary algorithms, enhanced computational and mathematical models. Abstract data types are introduced for genotypes in a genetic algorithm order to develop complex models using generative and evolutionary computing techniques. Multi-objective optimisation techniques are employed for defining the fitness function in the evaluation process.