With recent advances in the field of music informatics, approaches to audio-based music
structural analysis have matured considerably, allowing researchers to reassess the …

A great number of deep learning based models have been recently proposed for automatic
music composition. Among these models, the Transformer stands out as a prominent …

Automatic music transcription (AMT) is the task of transcribing audio recordings into
symbolic representations. Recently, neural network-based methods have been applied to …

Automatic Music Transcription has seen significant progress in recent years by training
custom deep neural networks on large datasets. However, these models have required …

Symbolic music datasets are important for music information retrieval and musical analysis.
However, there is a lack of large-scale symbolic datasets for classical piano music. In this …

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 …

Taking long-term spectral and temporal dependencies into account is essential for automatic
piano transcription. This is especially helpful when determining the precise onset and offset …

Most of the current supervised automatic music transcription (AMT) models lack the ability to
generalize. This means that they have trouble transcribing real-world music recordings from …

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 …

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 …