TOWARDS MUSICAL INSTRUMENT SEPARATION USING MULTIPLE-CAUSE NEURAL NETWORKS
J Klingseisen and M D Plumbley y
mark.plumbley@kcl.ac.uk
Over the last few years, interest has been growing
in neural network circles in the separation of indepen-
dent sources, using techniques such as blind source sep-
aration and independent component analysis (ICA).
A related technique is the `Multiple-Cause Model' of
Saund [Neural Computation, 7, 51-71, 1995]. In this
technique, a neural network is trained to model the ob-
served pattern as a composition of several underlying
`causes', in contrast to the more traditional `winner-
takes-all' neural networks which can handle only a sin-
gle `cause'. In this paper, we report on experiments
working towards the use of a simple multiple-cause
model with constraints to separate di erent instruments
and notes from audio spectral representations.