The capability of transcribing music audio into music notation is a fascinating example of
human intelligence. It involves perception (analyzing complex auditory scenes), cognition …

Following their success in Computer Vision and other areas, deep learning techniques have
recently become widely adopted in Music Information Retrieval (MIR) research. However …

We advance the state of the art in polyphonic piano music transcription by using a deep
convolutional and recurrent neural network which is trained to jointly predict onsets and …

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

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

What audio embedding approach generalizes best to a wide range of downstream tasks
across a variety of everyday domains without fine-tuning? The aim of the HEAR benchmark …

This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of
supervision and evaluation of machine learning methods for music research. MusicNet …

In this paper, we present nnAudio, a new neural network-based audio processing framework
with graphics processing unit (GPU) support that leverages 1D convolutional neural …

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 …

Multi-instrument automatic music transcription (AMT) is a critical but less investigated
problem in the field of music information retrieval (MIR). With all the difficulties faced by …