  Research in collaborative music learning is subject to unresolved problems
demanding new technological solutions. One such problem poses the suppression
of the accompaniment in a live recording of a performance during practice,
which can be for the purposes of self-assessment or further machine-aided
analysis. Being able to separate a solo from the accompaniment allows to create
learning agents that may act as personal tutors and help the apprentice improve
his or her technique. First, we start from the classical adaptive noise
cancelling approach, and adjust it to the problem at hand. In a second step, we
compare some adaptive and Wiener filtering approaches and assess their
performances on the task. Our findings underpin that adaptive filtering is
inapt of dealing with music signals and that Wiener filtering in the short-time
Fourier transform domain is a much more effective approach. In addition, it is
very cheap if carried out in the frequency bands of auditory filters. A
double-output extension based on maximal-ratio combining is also proposed.
