Semi-supervised Convolutive NMF for Automatic Piano Transcription
Creators
- 1. INSA Rennes
- 2. Univ. Rennes
- 3. Univ. Lyon, UJM Saint Etienne
Description
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.
Files
49.pdf
Files
(618.9 kB)
Name | Size | Download all |
---|---|---|
md5:07bd50d0c88390eb7252c20d78fcad39
|
618.9 kB | Preview Download |