Music Modeling
( Image credit: R-Transformer )
Leaderboards
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Greatest papers with code
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• locuslab/TCN
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Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
LANGUAGE MODELLING MACHINE TRANSLATION MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
R-Transformer: Recurrent Neural Network Enhanced Transformer
ICLR 2020
• DSE-MSU/R-transformer
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Recurrent Neural Networks have long been the dominating choice for sequence modeling.
SOTA for Music Modeling on Nottingham
LANGUAGE MODELLING MUSIC MODELING SEQUENTIAL IMAGE CLASSIFICATION
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• michaelfarrell76/End-To-End-Generative-Dialogue
• 
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs).
Sequential Neural Models with Stochastic Layers
How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks?
Generative timbre spaces: regularizing variational auto-encoders with perceptual metrics
Conference 2018
• acids-ircam/variational-timbre
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Based on this, we introduce a method for descriptor-based synthesis and show that we can control the descriptors of an instrument while keeping its timbre structure.
AUDIO CLASSIFICATION AUDIO GENERATION MUSIC INFORMATION RETRIEVAL MUSIC MODELING
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
ICLR 2019
• BShakhovsky/PolyphonicPianoTranscription
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Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
Diagonal RNNs in Symbolic Music Modeling
In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
LSTM: A Search Space Odyssey
Several variants of the Long Short-Term Memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995.
Improving Polyphonic Music Models with Feature-Rich Encoding
We show that training a neural network to predict a seemingly more complex sequence, with extra features included in the series being modeled, can improve overall model performance significantly.
SOTA for Music Modeling on JSB Chorales
Music Transformer
ICLR 2019
• Chatha-Sphere/pno-ai
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This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length.
#2 best model for
Music Modeling
on JSB Chorales