This paper describes the ideas and methods that led to the writing of an
algorithmic composition NTrope Suite. This piece, for solo
recorders and voice, was generated by ‘mixing’ works by
different composers from different eras. The idea behind the work was to
examine random generation procedures that could maintain stylistic
properties typical to the reference works. The interesting property of
this method is that it implements a sort of ‘statistical
learning’, that optimally preserves the properties of the reference
pieces and also properly ‘generalises’ them so as to create a
new valid work. The musical result is very coherent, maintaining both
stylistic resemblance to the reference music and exhibiting some surprising
originality as well. Theoretically, the resulting piece is closest to the
reference works in terms of mutual entropy. The algorithm and its
theoretical significance are discussed in the paper.