Onsets and frames: Dual-objective piano transcription

C Hawthorne, E Elsen, J Song, A Roberts… - ar** the frame-level: Event-based piano transcription with neural semi-crfs
Y Yan, F Cwitkowitz, Z Duan - Advances in Neural …, 2021 - proceedings.neurips.cc
Piano transcription systems are typically optimized to estimate pitch activity at each frame of
audio. They are often followed by carefully designed heuristics and post-processing …

Learning audio-sheet music correspondences for score identification and offline alignment

M Dorfer, A Arzt, G Widmer - arxiv preprint arxiv:1707.09887, 2017 - arxiv.org
This work addresses the problem of matching short excerpts of audio with their respective
counterparts in sheet music images. We show how to employ neural network-based cross …

Hppnet: Modeling the harmonic structure and pitch invariance in piano transcription

W Wei, P Li, Y Yu, W Li - arxiv preprint arxiv:2208.14339, 2022 - arxiv.org
While neural network models are making significant progress in piano transcription, they are
becoming more resource-consuming due to requiring larger model size and more …

Investigating the perceptual validity of evaluation metrics for automatic piano music transcription

A Ycart, L Liu, E Benetos, M Pearce - Transactions of the …, 2020 - qmro.qmul.ac.uk
Automatic Music Transcription (AMT) is usually evaluated using low-level criteria, typically
by counting the numbers of errors, with equal weighting. Yet, some errors (eg out-of-key …

Polyphonic pitch tracking with deep layered learning

A Elowsson - The Journal of the Acoustical Society of America, 2020 - pubs.aip.org
This article presents a polyphonic pitch tracking system that is able to extract both framewise
and note-based estimates from audio. The system uses several artificial neural networks …