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 ($\{p|p\in D\land p+v\in D\}$). 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 $p_{\phi}^0(\cdot)$.
#### 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.

or

By clicking below, you agree to our terms of service.

Sign in via Facebook
Sign in via Twitter
Sign in via GitHub
Sign in via Dropbox
Sign in with Wallet

Wallet
(
)

Connect another wallet
New to HackMD? Sign up