Music Modeling
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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
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• michaelfarrell76/End-To-End-Generative-Dialogue
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In this paper we compare different types of recurrent units in recurrent neural networks (RNNs).
Sequential Neural Models with Stochastic Layers
NeurIPS 2016 • marcofraccaro/srnn
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
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.
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
ICLR 2019
• junhoyeo/magenta-school-song
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Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
Deep Learning for Music
Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans.