Artist detection in music with Minnowmatch

Author

Whitman, Brian ; Flake, Gary ; Lawrence, Steve

Author_Institution

NEC Res. Inst., Princeton, NJ, USA

fYear

2001

fDate

2001

Firstpage

559

Lastpage

568

Abstract

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

Keywords

audio coding; information retrieval; learning (artificial intelligence); learning automata; music; neural nets; Mima; Minnowmatch; artist detection; audio databases; audio information retrieval systems; classifications; copyright protection devices; machine listening; machine listening and music retrieval engine; meta-data; music; music retrieval; neural networks; one-in-n artist space; recommendation agents; support vector machines; Audio databases; Copyright protection; Engines; Multiple signal classification; Music information retrieval; National electric code; Neural networks; Space technology; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing XI, 2001. Proceedings of the 2001 IEEE Signal Processing Society Workshop

Conference_Location

North Falmouth, MA

ISSN

1089-3555

Print_ISBN

0-7803-7196-8

Type

conf

DOI

10.1109/NNSP.2001.943160

Filename

943160