Music Transcription
33 papers with code • 1 benchmarks • 7 datasets
Music transcription is the task of converting an acoustic musical signal into some form of music notation.
( Image credit: ISMIR 2015 Tutorial - Automatic Music Transcription )
Benchmarks
These leaderboards are used to track progress in Music Transcription
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Music Transcription models and implementations
MAESTRO
MusicNet
Music21
CocoChorales
JS Fake Chorales
ErhuPT
EGDB
Most implemented papers
Deep Complex Networks
Despite their attractive properties and potential for opening up entirely new neural architectures, complex-valued deep neural networks have been marginalized due to the absence of the building blocks required to design such models.
MT3: Multi-Task Multitrack Music Transcription
magenta/mt3
•
•
ICLR 2022
Automatic Music Transcription (AMT), inferring musical notes from raw audio, is a challenging task at the core of music understanding.
Music transcription modelling and composition using deep learning
We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition.
Learning Features of Music from Scratch
benadar293/benadar293.github.io
•
•
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.
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
LUMII-Syslab/RSE
•
•
Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences.
The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy
We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks.
Sequence-to-Sequence Piano Transcription with Transformers
Aolin-MIR/Sequence-to-Sequence-Piano-Transcription-with-Transformers
•
•
Automatic Music Transcription has seen significant progress in recent years by training custom deep neural networks on large datasets.
An End-to-End Neural Network for Polyphonic Piano Music Transcription
We compare performance of the neural network based acoustic models with two popular unsupervised acoustic models.
Deep convolutional neural networks for predominant instrument recognition in polyphonic music
We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length.
Optimal spectral transportation with application to music transcription
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates.
