Artist Detection in Music with Minnowmatch

We demonstrate the artist detection component of Minnowmatch, a machine listening and music retrieval engine. Minnowmatch (Mima) automatically determines various metadata 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% of the time with 32 songs from 5 artists, and 70% of the time with 50 songs from 10 artists, where the artists were chosen in order to make the problem challenging. We show that scaling problems when using neural networks for classification can be reduced with a pre-classification step using multiple support vector machines.