Automatic Music Transcription and Audio Source Separation

1 September 2002

journal article
research article
Published by Taylor & Francis in Cybernetics and Systems

Vol. 33 (6) , 603-627
https://doi.org/10.1080/01969720290040777

Abstract

In this article, we give an overview of a range of approaches to the analysis and separation of musical audio. In particular, we consider the problems of automatic music transcription and audio source separation, which are of particular interest to our group. Monophonic music transcription, where a single note is present at one time, can be tackled using an autocorrelation-based method. For polyphonic music transcription, with several notes at any time, other approaches can be used, such as a blackboard model or a multiple-cause/sparse coding method. The latter is based on ideas and methods related to independent component analysis (ICA), a method for sound source separation.

Keywords

This publication has 17 references indexed in Scilit:

Volume Information
19th-Century Music, 2000
A blackboard architecture for computational auditory scene analysis
Speech Communication, 1999
Structured audio: creation, transmission, and rendering of parametric sound representations
Proceedings of the IEEE, 1998
Modelling multiple-cause structure using rectification constraints
Network: Computation in Neural Systems, 1998
Time-frequency analysis of musical signals
Proceedings of the IEEE, 1996
Natural image statistics and efficient coding
Network: Computation in Neural Systems, 1996
Development of low entropy coding in a recurrent network
Network: Computation in Neural Systems, 1996
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Neural Computation, 1995
A Multiple Cause Mixture Model for Unsupervised Learning
Neural Computation, 1995
Musical frequency tracking using the methods of conventional and “narrowed” autocorrelation
The Journal of the Acoustical Society of America, 1991