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 …

Most of the state-of-the-art automatic music transcription (AMT) models break down the main
transcription task into sub-tasks such as onset prediction and offset prediction and train them …

Recent advances in polyphonic piano transcription have been made primarily by a
deliberate design of neural network architectures that detect different note states such as …

Abstract The field of Music Information Retrieval (MIR) focuses on creating methods and
practices for making sense of music data from various modalities, including audio, video …

The automatic music transcription (AMT) task is designed to convert raw performance audio
signals into digital representations of symbolic music for possible computational musicology …

This paper describes an automatic drum transcription (ADT) method that directly estimates a
tatum-level drum score from a music signal in contrast to most conventional ADT methods …

Current state-of-the-art methods for polyphonic piano transcription tend to use high capacity
neural networks. Most models are trained “end-to-end”, and learn a mapping from audio …

Music language models play an important role for various music signal and symbolic music
processing tasks, such as music generation, symbolic music classification, or automatic …

In recent years, advancements in neural network designs and the availability of large-scale
labeled datasets have led to significant improvements in the accuracy of piano transcription …

This paper describes a neural drum transcription method that detects from music signals the
onset times of drums at the tatum level, where tatum times are assumed to be estimated in …