Temperley D. and D. Sleator, “Modeling Meter and Harmony: A
Preference-Rule Approach,” Computer Music Journal, 23(1), 10–27, Spring
1999.
Abstract
Computational music analysis is an important project for a number of
reasons. From a psychological point of view, systems for performing
musical processes may shed light on how human listeners perform such
processes, just as computational work in vision and problem solving has
led to important insights in those domains. While recent research has
revealed much about listeners’ mental representations of music, there
is much to be learned in this area. There are practical applications to
automatic music analysis as well. Many kinds of music-related tasks
that computers might perform—for example, producing a score from raw
pitch data, detecting probable errors in a score, searching a melodic
database for matches to a query, generating an accompaniment for a
melody, or performing statistical analysis of musical styles for the
purposes of automatic composition—require an ability to process music
and extract various kinds of structure and information from it.
Extracting this information proves to be a highly complex task.
In this article, we present a computational system for analyzing
metrical and harmonic structure. The system is designed for Western
tonal music, particularly art music of the “common-practice” period.