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.
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…
Cluster analysis is a family of statistical and computational methods for answering a deceptively simple question: Given a collection of things described by several characteristics, can we discover natural groups among them without deciding beforehand what those groups are? In other words, cluster analysis starts without known labels. You can understand why cluster analysis is…