Shaofan Lai
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Shaofan Lai
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Jun 18, 2018
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MAAI reading
Music generation
LEARNING A LATENT SPACE OF MULTITRACK MEASURES
Encoder: Two layer of bidirectional LSTM
State2Latent: 2 FC
Decoder: Two layer of unidirectional LSTM
Chord2Vec: Learning Musical Chord Embeddings
Using Bilinear, auto-regressive and seq2seq model to embed a chord by predicting the given chord's content.
[ISMIR 2017] Generating Nontrivial Melodies for Music as a Service
Conditional VAE (condition on the chord progression)
Split the melody/chord using handcrafted metric
Song From PI: a musically plausible network for pop music generation
Simple hierarchy: stacked LSTM with higher level outputting chords and bottom level outputting keys.
Cluster chords, drum patterns and so on into clusters.
Some extension (applications).
Cosiatec and Siateccompress: Pattern Discovery by Geometric Compression
Is used in MorpheuS
Uses a shift vector
v
to group patterns (
{
p
|
p
∈
D
∧
p
+
v
∈
D
}
). Extract the best pattern (with a handcrafted metric) each time
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions
Conditional CNN GAN.
Uses feature matching to control the creativity.
Lots of pre-processing.
MorpheuS: generating structured music with constrained patterns and tension
Tension model in Spiral Array
cloud diameter
cloud momentum
tensile strain
Combination optimization with heuristic solver (VNS)
Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and translation
Deep Learning for music
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Tuning Recurrent Neural Networks with Reinforcement Learning
Sequence modeling
Improved variational inference with inverse autoregressive flow
Normalizing flow with Inverse autoregressive flow.
A type of variant inference (normalizing flow) that can handles data with high dimension.
Learning the base distribution in implicit generative models
Two stage training: autoencoder, encoded-space.
Confusing formula (5)(6)(7). Unclear definition of
p
Ï•
0
(
â‹…
)
.
Unsupervised Learning of Sequence Representation by Auto-encoders
Use seq2seq model to capture the holistic feature, use the CharRNN model to capture the local feature.
Shared LSTM module as encoder for both models and decoder for CharRNN.
Use stop signal to keep track the time-step.
Dilated RNN
https://github.com/umbrellabeach/music-generation-with-DL
Embedding
http://ruder.io/word-embeddings-2017/
"subword"
ConceptNet
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Multi-lingual
A Survey of Cross-lingual Word Embedding Models Sebastian
http://ruder.io/word-embeddings-1/
Training embedding is of high computational complexity when there are a lot s of elements.
Embedding trained along with the model can be task-specific.
The second last layer is actually a kind of embedding for the output word. But with different embedding of the input layer.
C&W model
Replace probability with score and use hinge loss as the loss function
Using the context to predict the score of the middle word. Only takes previous words.
Word2vec
no non-linearity
no deep structure
more context
A lot of training strategies
Takes previous and the next context.
CBOW
Using the context to predict the center word
No orders in the context.
Skip-gram
Using the center word to predict the context.
http://ruder.io/word-embeddings-softmax/index.html
To solve the overhead brought by the last decision layer.
Sampling
Notice: music elements have limited number of objects. So we don't have to accelerate the softmaxl layer.
Hierarchical Softmax (H-softmax)
Softmax as a sequence of softmax
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
Use TDNN and lookup table to train the NLP model
Window approach might hurt the long-term dependencies.
Embedding is trained along with the entire model.
MAAI reading
Music generation
LEARNING A LATENT SPACE OF MULTITRACK MEASURES
Chord2Vec: Learning Musical Chord Embeddings
[ISMIR 2017] Generating Nontrivial Melodies for Music as a Service
Song From PI: a musically plausible network for pop music generation
Cosiatec and Siateccompress: Pattern Discovery by Geometric Compression
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions
MorpheuS: generating structured music with constrained patterns and tension
Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and translation
Deep Learning for music
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Tuning Recurrent Neural Networks with Reinforcement Learning
Sequence modeling
Improved variational inference with inverse autoregressive flow
Learning the base distribution in implicit generative models
Unsupervised Learning of Sequence Representation by Auto-encoders
Dilated RNN
https://github.com/umbrellabeach/music-generation-with-DL
Embedding
http://ruder.io/word-embeddings-2017/
http://ruder.io/word-embeddings-1/
http://ruder.io/word-embeddings-softmax/index.html
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
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MAAI reading
Music generation
LEARNING A LATENT SPACE OF MULTITRACK MEASURES
Chord2Vec: Learning Musical Chord Embeddings
[ISMIR 2017] Generating Nontrivial Melodies for Music as a Service
Song From PI: a musically plausible network for pop music generation
Cosiatec and Siateccompress: Pattern Discovery by Geometric Compression
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions
MorpheuS: generating structured music with constrained patterns and tension
Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and translation
Deep Learning for music
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Tuning Recurrent Neural Networks with Reinforcement Learning
Sequence modeling
Improved variational inference with inverse autoregressive flow
Learning the base distribution in implicit generative models
Unsupervised Learning of Sequence Representation by Auto-encoders
Dilated RNN
https://github.com/umbrellabeach/music-generation-with-DL
Embedding
http://ruder.io/word-embeddings-2017/
http://ruder.io/word-embeddings-1/
http://ruder.io/word-embeddings-softmax/index.html
A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning
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