Abstract
We describe two multi-agent architectures for an improvisation oriented musician-machine interaction systems that learn in real time from human performers. The improvisation kernel is based on sequence modeling and statistical learning. We present two frameworks of interaction with this kernel. In the first, the stylistic interaction is guided by a human operator in front of an interactive computer environment. In the second framework, the stylistic interaction is delegated to machine intelligence and therefore, knowledge propagation and decision are taken care of by the computer alone. The first framework involves a hybrid architecture using two popular composition/performance environments, Max and OpenMusic, that are put to work and communicate together, each one handling the process at a different time/memory scale. The second framework shares the same representational schemes with the first but uses an Active Learning architecture based on collaborative, competitive and memory-based learning to handle stylistic interactions. Both systems are capable of processing real-time audio/video as well as MIDI. After discussing the general cognitive background of improvisation practices, the statistical modelling tools and the concurrent agent architecture are presented. Then, an Active Learning scheme is described and considered in terms of using different improvisation regimes for improvisation planning. Finally, we provide more details about the different system implementations and describe several performances with the system.
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Notes
- 1.
It should be noted that the use of the term Learning in AL refers to the ability to adaptively select the best repertoire for improvisation (we refer to this as second mode), which is different from the learning aspect involved in construction of the musical dynamic memory that is central to the first mode of operation. The term learning refers here to learning of the criteria or the costs involved in selection of repertoire that would be appropriate for interaction, and it should be distinguished from the learning involved in forming the stylistic memory. The reason for using this term in AL is for its common use in the artificial intelligence literature.
- 2.
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Assayag, G., Bloch, G., Cont, A., Dubnov, S. (2010). Interaction with Machine Improvisation. In: Argamon, S., Burns, K., Dubnov, S. (eds) The Structure of Style. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12337-5_10
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