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v2/README.md

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+# MuseGAN
+
+<font color=red><strong><i>Warning: this version is no longer maintained</i></strong></font>
+
+[MuseGAN](https://salu133445.github.io/musegan/) is a project on music
+generation. In essence, we aim to generate polyphonic music of multiple tracks
+(instruments) with harmonic and rhythmic structure, multi-track interdependency
+and temporal structure. To our knowledge, our work represents the first approach
+that deal with these issues altogether.
+
+The models are trained with
+[Lakh Pianoroll Dataset](https://salu133445.github.io/lakh-pianoroll-dataset/)
+(LPD), a new multi-track piano-roll dataset, in an unsupervised approach. The
+proposed models are able to generate music either from scratch, or by
+accompanying a track given by user. Specifically, we use the model to generate
+pop song phrases consisting of bass, drums, guitar, piano and strings tracks.
+
+Sample results are available [here](https://salu133445.github.io/musegan/results).
+
+## BinaryMuseGAN
+
+[BinaryMuseGAN](https://salu133445.github.io/bmusegan/) is a follow-up project
+of the [MuseGAN](https://salu133445.github.io/musegan/) project.
+
+In this project, we first investigate how the real-valued piano-rolls generated
+by the generator may lead to difficulties in training the discriminator for
+CNN-based models. To overcome the binarization issue, we propose to append to
+the generator an additional refiner network, which try to refine the real-valued
+predictions generated by the pretrained generator to binary-valued ones. The
+proposed model is able to directly generate binary-valued piano-rolls at test
+time.
+
+We trained the network with
+[Lakh Pianoroll Dataset](https://salu133445.github.io/lakh-pianoroll-dataset/)
+(LPD). We use the model to generate four-bar musical phrases consisting of eight
+tracks: *Drums*, *Piano*, *Guitar*, *Bass*, *Ensemble*, *Reed*, *Synth Lead* and
+*Synth Pad*. Audio samples are available
+[here](https://salu133445.github.io/bmusegan/samples).
+
+## Run the code
+
+### Prepare Training Data
+
+- Prepare your own data or download our training data
+
+  The array will be reshaped to (-1, `num_bar`, `num_timestep`, `num_pitch`,
+  `num_track`). These variables are defined in `config.py`.
+
+  - [`lastfm_alternative_5b_phrase.npy`](https://drive.google.com/uc?export=download&id=1F7J5n9uOPqViBYpoPT5GvE4PjCWhOyWc) (2.1 GB)
+    contains 12,444 four-bar phrases from 2,074 songs with *alternative* tags.
+    The shape is (2074, 6, 4, 96, 84, 5). The five tracks are *Drums*, *Piano*,
+    *Guitar*, *Bass* and *Strings*.
+  - [`lastfm_alternative_8b_phrase.npy`](https://drive.google.com/uc?export=download&id=1x3CeSqE6ElWa6V7ueNl8FKPFmMoyu4ED) (3.6 GB)
+    contains 13,746 four-bar phrases from 2,291 songs with *alternative* tags.
+    The shape is (2291, 6, 4, 96, 84, 8). The eight tracks are *Drums*, *Piano*,
+    *Guitar*, *Bass*, *Ensemble*, *Reed*, *Synth Lead* and *Synth Pad*.
+  - Download the data with this [script](training_data/download.sh).
+
+- (optional) Save the training data to shared memory with this [script](training_data/store_to_sa.py).
+
+- Specify training data path and location in `config.py`. (see below)
+
+### Configuration
+
+Modify `config.py` for configuration.
+
+- Quick setup
+
+  Change the values in the dictionary `SETUP` for a quick setup. Documentation
+  is provided right after each key.
+
+- More configuration options
+
+  Four dictionaries `EXP_CONFIG`, `DATA_CONFIG`, `MODEL_CONFIG` and
+  `TRAIN_CONFIG` define experiment-, data-, model- and training-related
+  configuration variables, respectively.
+
+  > The automatically-determined experiment name is based only on the values
+defined in the dictionary `SETUP`, so remember to provide the experiment name
+manually (so that you won't overwrite a trained model).
+
+### Run
+
+```sh
+python main.py
+```
+
+## Papers
+
+- Hao-Wen Dong and Yi-Hsuan Yang,
+  "Convolutional Generative Adversarial Networks with Binary Neurons for
+  Polyphonic Music Generation,"
+  to appear at *International Society for Music Information Retrieval Conference*
+  (ISMIR), 2018.
+  [[website](https://salu133445.github.io/bmusegan/)]
+  [[arxiv](https://arxiv.org/abs/1804.09399)]
+  [[slides](https://salu133445.github.io/bmusegan/pdf/bmusegan-tmacw2018-slides.pdf)]
+
+- Hao-Wen Dong\*, Wen-Yi Hsiao\*, Li-Chia Yang and Yi-Hsuan Yang,
+  "MuseGAN: Multi-track Sequential Generative Adversarial Networks for
+  Symbolic Music Generation and Accompaniment,"
+  in *AAAI Conference on Artificial Intelligence* (AAAI), 2018.
+  [[website](https://salu133445.github.io/musegan/)]
+  [[arxiv](http://arxiv.org/abs/1709.06298)]
+  [[slides](https://salu133445.github.io/musegan/pdf/musegan-aaai2018-slides.pdf)]
+
+- Hao-Wen Dong\*, Wen-Yi Hsiao\*, Li-Chia Yang and Yi-Hsuan Yang,
+  "MuseGAN: Demonstration of a Convolutional GAN Based Model for Generating
+  Multi-track Piano-rolls,"
+  in *ISMIR Late-Breaking and Demo Session*, 2017.
+  (non-peer reviewed two-page extended abstract)
+  [[paper](https://salu133445.github.io/musegan/pdf/musegan-ismir2017-lbd-paper.pdf)]
+  [[poster](https://salu133445.github.io/musegan/pdf/musegan-ismir2017-lbd-poster.pdf)]
+
+\* *These authors contributed equally to this work.*