Generating Music from Flocking Dynamics

Abstract

While it may be impossible to determine precisely what defines appealing and interesting music, it can be argued that, in order to have these qualities, sounds should contain enough elements of order and disorder, coherence and decoherence, or structure and variation throughout a musical piece. These can appear in different ways and at various levels; in the interplay between expected and surprising note sequences found in engaging melodies, in the superposition of perfectly and imperfectly coinciding harmonics required for a rich chord, or in the mixture of regular and syncopated rhythms that build a driving percussion layer, to name a few. Another important component is the inclusion of multiple elements that can both combine into a coherent musical structure or disband into independently acting parts. Music is often based entirely on fragile harmonic or rhythmic structures that fluctuate between states of order and disorder, constantly consolidating and collapsing. Composers explicitly explore these extremes, for example, in the interplay between instruments in an orchestra, between melodic lines in counterpoint [1], between collective and solo parts in a Jazz performance [2], or in the dense chromatic melodies of some contemporary music [3].

Advances in Computer Entertainment Technology

“…In these approaches the control changes are infrequent and irregular with respect to the simulation time step, and the user does not interact directly with individual agents. Other work using agents in real-time performance includes EvoMove [22][23][24][25][26][27], which applies a commensal computing scheme to provide a movement-based musical companion for dancers, with sounds conceived as ambient feedback rather than presenting a musical structure.…”

Advancing Performability in Playable Media: A Simulation-based Interface as a Dynamic Score

“…These approaches are best summarized by method of extracting control data, rather than method of sound generation, which can vary enormously. The most common approach is data extracted from individual agents to control individual sound sources [14,15,16,17,18]. This approach has been termed a direct [18] or literal mapping [19] of data to sound.…”

“…The most common approach is data extracted from individual agents to control individual sound sources [14,15,16,17,18]. This approach has been termed a direct [18] or literal mapping [19] of data to sound. Each agent makes sound independently based upon absolute position in the swarm phase space.…”

“…Alternately a single sound source may be controlled by extracting data from behaviours of multiple agents, to enable independent scalability of sound sources and agents. Physics-based methods for this approach are discussed in [17,18] and statistical feature extraction [20] is discussed in this paper.…”