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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
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 …
audio. They are often followed by carefully designed heuristics and post-processing …
Learning audio-sheet music correspondences for score identification and offline alignment
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 …
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
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 …
becoming more resource-consuming due to requiring larger model size and more …
Investigating the perceptual validity of evaluation metrics for automatic piano music transcription
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 …
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 …
and note-based estimates from audio. The system uses several artificial neural networks …