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@InProceedings{donze-icmc14,
author = {Alexandre Donz{\'{e}} and Rafael Valle and Ilge Akkaya and Sophie Libkind and Sanjit A. Seshia and David Wessel},
title = {Machine Improvisation with Formal Specifications},
booktitle = {Proceedings of the 40th International Computer Music Conference (ICMC)},
OPTcrossref = {},
OPTkey = {},
pages = {1277--1284},
year = {2014},
OPTeditor = {},
OPTvolume = {},
OPTnumber = {},
OPTseries = {},
OPTaddress = {},
month = {September},
OPTorganization = {},
OPTpublisher = {},
note = {Available online at \url{http://hdl.handle.net/2027/spo.bbp2372.2014.196}.},
OPTannote = {},
abstract={We define the problem of machine improvisation of music
with formal specifications. In this problem, one seeks to
create a random improvisation of a given reference melody
that however satisfies a specification encoding constraints
that the generated melody must satisfy. More specifically,
we consider the scenario of generating a monophonic Jazz
melody (solo) on a given song harmonization. The music
is encoded symbolically, with the improviser generating a
sequence of note symbols comprising pairs of pitches (frequencies)
and discrete durations. Our approach can be decomposed
roughly into two phases: a generalization phase,
that learns from a training sequence (e.g., obtained from
a human improviser) an automaton generating similar sequences,
and a supervision phase that enforces a specification
on the generated sequence, imposing constraints on
the music in both the pitch and rhythmic domains. The supervision
uses a measure adapted from Normalized Compression
Distances (NCD) to estimate the divergence between
generated melodies and the training melody and employs
strategies to bound this divergence. An empirical
evaluation is presented on a sample set of Jazz music.},
}