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Research in collaborative music learning is subject to unresolved problemsdemanding new technological solutions. One such problem poses the suppressionof the accompaniment in a live recording of a performance during practice,which can be for the purposes of self-assessment or further machine-aidedanalysis. Being able to separate a solo from the accompaniment allows to createlearning agents that may act as personal tutors and help the apprentice improvehis or her technique. First, we start from the classical adaptive noisecancelling approach, and adjust it to the problem at hand. In a second step, wecompare some adaptive and Wiener filtering approaches and assess theirperformances on the task. Our findings underpin that adaptive filtering isinapt of dealing with music signals and that Wiener filtering in the short-timeFourier transform domain is a much more effective approach. In addition, it isvery cheap if carried out in the frequency bands of auditory filters. Adouble-output extension based on maximal-ratio combining is also proposed.

SISO and SIMO Accompaniment Cancellation for Live Solo Recordings Based on Short-Time ERB-Band Wiener Filtering and Spectral Subtraction