Semi-Supervised Convolutive NMF for Automatic Music Transcription

• URL: http://arxiv.org/abs/2202.04989v1
• Date: Thu, 10 Feb 2022 12:38:53 GMT
• Title: Semi-Supervised Convolutive NMF for Automatic Music Transcription
• Authors: Haoran Wu, Axel Marmoret, J\'er\'emy E. Cohen
• Abstract summary: We propose a semi-supervised approach using low-rank matrix factorization techniques, in particular Convolutive Nonnegative Matrix Factorization. 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.
• Score: 6.583111368144214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, 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.

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