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Automatic Music Transcription, which consists in transforming an audiorecording of a musical performance into symbolic format, remains a difficultMusic Information Retrieval task. In this work, which focuses on pianotranscription, we propose a semi-supervised approach using low-rank matrixfactorization techniques, in particular Convolutive Nonnegative MatrixFactorization. In the semi-supervised setting, only a single recording of eachindividual notes is required. We show on the MAPS dataset that the proposedsemi-supervised CNMF method performs better than state-of-the-art low-rankfactorization techniques and a little worse than supervised deep learningstate-of-the-art methods, while however suffering from generalization issues.

Semi-Supervised Convolutive NMF for Automatic Piano Transcription