Active learning for interactive music transcription

Manually transcribing music is labour intensive, but still the dominant way of creating data to train an automatic transcription system (in the hope that one day it could replace manual transcription). One reason why the manual process is so time-consuming, is that annotations are typically made for entire music pieces, even for sections that in hindsight contribute little to the improvement of an automatic system because the previous version of the system already managed to successfully transcribe that section. The underlying cause is that the current state-of-the-art in music transcription, regardless of the exact characteristic that's being transcribed (e.g. tempo, melody, instrumentation, harmony), is based on deep learning techniques, which are not very good at representing uncertainty. The field of active learning aims to include uncertainty as part of the training process, such that an iterative workflow can be established where the segment that is deemed most informative gets presented for transcription first. In this project, the leading active learning approaches will be adapted to one or more music transcription tasks. The result will be integrated into a browser-based transcription tool, which will subsequently be used to study the difference in personal preferences and subjectivity between transcribers.