31 Amelie Anglade Simon Dixon 2009 London Hopper Colloquium The explosion of the size of personal and commercial music collections has left both content providers and customers with a common difficulty: organising their huge musical libraries in such a way that each song can be easily retrieved, recommended and included in a playlist with similar songs. Because classifying large amounts of data is expensive and/or time-consuming, people are gaining interest in the automatic characterisation of songs. We present the first step towards a framework able to automatically induce rules characterising songs by various musical phenomena (e.g. rhythm, harmony, structure, etc.). For this study we are interested in the automatic extraction of harmony patterns. We analyse manually annotated chord data available in RDF and interlinked with web identifiers which themselves give access to a detailed description of the chords. We pre-process these data to obtain high-level information before passing them to an Inductive Logic Programming software which extracts the harmony rules underlying them. This framework is tested over the Real Book (jazz) songs and the Beatles' (pop) music. It generates a total over several experiments of 12,450 harmony rules characterising and differentiating these two datasets. An analysis of the most common rules reveals a list of well-known pop and jazz patterns. This poster won the first prize of the poster competition (open to PhD students and postdoctoral researchers).