  The popularity of applying machine learning techniques in musical domains has
created an inherent availability of freely accessible pre-trained neural
network (NN) models ready for use in creative applications. This work outlines
the implementation of one such application in the form of an assistance tool
designed for live improvisational performances by laptop ensembles. The primary
intention was to leverage off-the-shelf pre-trained NN models as a basis for
assisting individual performers either as musical novices looking to engage
with more experienced performers or as a tool to expand musical possibilities
through new forms of creative expression. The system expands upon a variety of
ideas found in different research areas including new interfaces for musical
expression, generative music and group performance to produce a networked
performance solution served via a web-browser interface. The final
implementation of the system offers performers a mixture of high and low-level
controls to influence the shape of sequences of notes output by locally run NN
models in real time, also allowing performers to define their level of
engagement with the assisting generative models. Two test performances were
played, with the system shown to feasibly support four performers over a four
minute piece while producing musically cohesive and engaging music. Iterations
on the design of the system exposed technical constraints on the use of a
JavaScript environment for generative models in a live music context, largely
derived from inescapable processing overheads.
