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Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
- Publication Year :
- 2021
-
Abstract
- This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Given a polyphonic multi-instrumental music signal along with its ground truth or predicted notes, the objective is to assign an instrumental source for each note. This problem is addressed as a pitch-informed classification task where each note is analysed individually. We also propose to utilise several kernel shapes in the convolutional layers in order to facilitate learning of efficient timbre-discriminative feature maps. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904 when the original multi-pitch annotations are used as the pitch information for the system, and that it also excels if the note information is provided using third-party multi-pitch estimation algorithms. We also include ablation studies investigating the effects of the use of multiple kernel shapes and comparing different input representations for the audio and the note-related information.<br />Comment: 4 figures, 4 tables and 7 pages. Accepted for publication at ISMIR Conference 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2107.13617
- Document Type :
- Working Paper