Artist detection in music with Minnowmatch

In this paper we demonstrate the artist detection component of Minnowmatch, a machine listening and music retrieval engine. Minnowmatch (Mima) automatically determines various meta-data and makes classifications concerning a piece of audio using neural networks and support vector machines. The technologies developed in Minnowmatch may be used to create audio information retrieval systems, copyright protection devices, and recommendation agents. This paper concentrates on the artist or source detection component of Mima, which we show to classify a one-in-n artist space correctly 91% over a small song-set and 70% over a larger song set. We show that scaling problems using only neural networks for classification can be addressed with a pre-classification step of multiple support vector machines.