Pitch-Informed Instrument Assignment using a Deep Convolutional Network with Multiple Kernel Shapes.
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Editors
Lee, JH
Lerch, A
Duan, Z
Nam, J
Rao, P
Kranenburg, PV
Srinivasamurthy, A
Pagination
389 - 395
Publisher
Publisher URL
ISBN-13
978-1-7327299-0-2
Metadata
Show full item recordAbstract
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.