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Pitch-Informed Inst...
Pitch-Informed Instrument Assignment using a Deep Convolutional Network with Multiple Kernel Shapes
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- Ahlbäck, Sven, 1960- (author)
- Kungl. Musikhögskolan,Institutionen för folkmusik,Kungliga Musikhögskolan, Stockholm
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Kungl Musikhögskolan Institutionen för folkmusik (creator_code:org_t)
- 2021
- 2021
- English.
- Related links:
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https://urn.kb.se/re...
Abstract
Subject headings
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- 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 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
Keyword
- Harmonic-Percussive Source Separation
- Music Information Retrieval
Publication and Content Type
- ref (subject category)
- kon (subject category)
- kfu (subject category)
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