Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes

• URL: http://arxiv.org/abs/2107.13617v1
• Date: Wed, 28 Jul 2021 19:48:09 GMT
• Title: Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes
• Authors: Carlos Lordelo, Emmanouil Benetos, Simon Dixon and Sven Ahlb\"ack
• Abstract summary: This paper proposes a deep convolutional neural network for performing note-level instrument assignment. Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904.
• Score: 22.14133334414372
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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