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Communication Dans Un Congrès Année : 2022

Semi-Supervised Convolutive NMF for Automatic Piano Transcription

Résumé

Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task. In this work, which focuses on piano transcription, we propose a semi-supervised approach using low-rank matrix factorization techniques, in particular Convolutive Nonnegative Matrix Factorization. In the semi-supervised setting, only a single recording of each individual notes is required. We show on the MAPS dataset that the proposed semi-supervised CNMF method performs better than state-of-the-art low-rank factorization techniques and a little worse than supervised deep learning state-of-the-art methods, while however suffering from generalization issues.

Dates et versions

hal-03608497 , version 1 (14-03-2022)

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Citer

Haoran Wu, Axel Marmoret, Jérémy E. Cohen. Semi-Supervised Convolutive NMF for Automatic Piano Transcription. Sound and Music Computing 2022, Jun 2022, St Etienne, France. ⟨10.5281/zenodo.6798192⟩. ⟨hal-03608497⟩
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