This blog documents the process of teaching an AI system to generate melodies from the Skiptune database. Expect experiments, mistakes, and occasional surprises, and at the end expect melodies that sound as if a human wrote them.
Problem to Be Solved The Skiptune database is a database of 83,000 melodies from around the world and across several centuries. These melodies are encoded in a special way (a pitch difference followed by a duration ratio), and we want to explore whether an AI model can learn melodic structure from this representation. The…
We have our 25 metrics for the cluster analysis developed last week. The rest (18 metrics) have been archived for now, though we expect to return to them later for purposes other than cluster analysis. Three Prep Steps We have three tasks to perform with the 25 metrics before we proceed to the cluster analysis…
This week we evaluate the 43 metric candidates with a diagnostic program using a combination of statistics. After considering evidence from correlation runs, we combined that evidence with the musical meaning and implementation of each metric. The statistical tools we use are Pearson’s correlation and Spearman’s correlation, which correlated each metric with each other metric.…
Before using cluster analysis to see whether musical styles emerge naturally from the Skiptune database, we first needed to answer a more basic question: What characteristics of a tune should the computer measure? 34 Metrics Early on in the Skiptune project, as we were entering tunes, we found ourselves wondering if certain patterns we were…
This week we talk about how to apply cluster analysis to the Skiptune database. Skiptune is unusually well suited to clustering because we have both the symbolic melodies and extensive metadata. We distinguish clustering the tunes themselves from clustering composers, genres, historical periods, or musical fragments. The most interesting analysis, given what we’ve already built, would be…