On Algorithmic Representation of Music Style David Cope Professor of Music, Univ. of California, Santa Cruz

Overview  EMI (Experiments in Music Intelligence): represent music styles based on linguistic principles  Language Parsing  Augmented Transition Networks (ATN)  Non-linear composition  Future work

Language Parsing  Emphasis: hierarchy (most to least important) and abstraction of function  Ex. I am driving to the country. I am driving the country. I am driving to the ( ). I country.

Language Parsing (cont.) Hierarchical Parsing (Arrow: logical modifier processions) Abstraction of function

Musical Parallels Schenkerian layer analysis: prolongation of tonic from the first to the last C Hierarchical Parsing

Music Parallels (cont.) Abstraction of function EMI Symbology: OSAC (identifiers, priority order) O: ornamental S: statement A: antecedent C: consequent Level of importance: 1-3 (least to most significant )

Augmented Transition Networks (ATN)  Allow finer detail between context-free and context driven environments

ATN (cont.)  Music example

Non-linear Composition  Top-down method Music has an end.  Mid-level communication No backtracking

Example  A Back-like keyboard invention

Future Works  Refine ATN engine  More accurate representations of style integrity