Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a
challenging task at the core of music understanding. Unlike Automatic Speech Recognition …

In this paper we present Aligned Scores and Performances (ASAP): a new dataset of 222
digital musical scores aligned with 1068 performances (more than 92 hours) of Western …

Multitrack music transcription aims to transcribe a music audio input into the musical notes of
multiple instruments simultaneously. It is a very challenging task that typically requires a …

Deep learning technology has been extensively studied for its potential in music, notably for
creative music generation research. Traditional music generation approaches based on …

We present a framework based on neural networks to extract music scores directly from
polyphonic audio in an end-to-end fashion. Most previous Automatic Music Transcription …

We present an automatic piano transcription system that converts polyphonic audio
recordings into musical scores. This has been a long-standing problem of music information …

This work presents an end-to-end method based on deep neural networks for audio-to-score
music transcription of monophonic excerpts. Unlike existing music transcription methods …

State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been
carried out using monophonic transcription techniques to handle complex score layouts …

To compliment the existing set of datasets, we present a small dataset entitled vocadito,
consisting of 40 short excerpts of monophonic singing, sung in 7 different languages by …

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 …