Missing template estimation for user-assisted music transcription

For a user-assisted music transcription system in which the user is asked to label some notes for each instrument in the recording, we investigate ways to limit the amount of information the user has to provide. Different methods are proposed and experimentally compared that enable the estimation of template spectra at pitch positions that have not been annotated by the user, in order to derive a full set of instrument templates that can be used within a non-negative matrix factorisation framework. A set of error metrics is presented that enables the evaluation of the NMF gain matrix. The results show that purely data-driven methods outperform more refined instrument models when the user annotates notes at many different pitches for each instrument. When notes are labelled at a smaller number of different pitches, the highest accuracies are obtained using pre-stored instrument templates that are adapted to the instruments in the mixture.