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arXiv · 2009.13729

Bespoke Neural Networks for Score-Informed Source Separation

Ethan Manilow·Bryan Pardo
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Abstract

In this paper, we introduce a simple method that can separate arbitrary musical instruments from an audio mixture. Given an unaligned MIDI transcription for a target instrument from an input mixture, we synthesize new mixtures from the midi transcription that sound similar to the mixture to be separated. This lets us create a labeled training set to train a network on the specific bespoke task. When this model applied to the original mixture, we demonstrate that this method can: 1) successfully separate out the desired instrument with access to only unaligned MIDI, 2) separate arbitrary instruments, and 3) get results in a fraction of the time of existing methods. We encourage readers to listen to the demos posted here: https://git.io/JUu5q.

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BibTeXRIS

Ethan Manilow, Bryan Pardo. 2020-09-29. Bespoke Neural Networks for Score-Informed Source Separation. https://arxiv.org/abs/2009.13729

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