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Deep Learning Model for Signal Data

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- transformer.png
- parse_file.py
- resample.py
- - multihead_attention.py
  - position_embedding.py
  - transformer.py
- model.py
- model_concat.py
- model_iq.py
- model_iq_concat.py
- train.py
- train_concat.py
- train_gen.py
- train_gen_concat.py
- train_gen_iq.py
- train_gen_iq_concat.py
- train_iq.py
- train_iq_concat.py
.gitignore
fnn_crelu.py
fnn_iq.py
gru.py
LICENSE
lstm_iq_gen.py
lstm_music_gen.py
models.py
README.md
rnn_iq.py
utils.py

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# Complex Transformer
A deep learning model which incorporates the transformer model as a backbone and develop complex attention and encoder/decoder network operating on complex-valued input. This model achieves state-of-the-art results on music transcription tasks. 
**Title:** Complex Transformer: A Framework for Modeling Complex-Valued Sequence 
**Arxiv:** https://arxiv.org/abs/1910.10202

## Requirement
python: `python=3.6` 
cuda : `cuda>=9.0` 
packages: `pip install numpy scipy sklearn intervaltree resampy torch` 
This code base relies on GPUs and cuda.
## Transformer model

 <img src="https://github.com/muqiaoyang/dl_signal/blob/master/img/transformer.png" alt="Complex Transformer"/>

## File parsing
`cd music/` 
`wget https://homes.cs.washington.edu/~thickstn/media/musicnet.npz` 
`python3 -u resample.py musicnet.npz musicnet_11khz.npz 44100 11000` 
`rm musicnet.npz` 
`python3 -u parse_file.py` 
`rm musicnet_11khz.npz` 
`cd ..` 
(This process is quite long)

## Instruction
Preprocess the MusicNet dataset as stated in the paper: 
`python parse_file.py`

Sample command line for automatic music transcription: 
`python -u transformer/train.py`

Concatenated transformer for automatic music transcription: 
`python -u transformer/train_concat.py`

For MusicNet generation tasks: 
`python -u transformer/train_gen.py`

Concatenated transformer for MusicNet generation: 
`python -u transformer/train_gen_concat.py`

For IQ classification task: 
`python -u transformer/train_iq.py`

Concatenated transformer for IQ classification task: 
`python -u transformer/train_iq_concat.py`

For IQ generation tasks: 
`python -u transformer/train_gen_iq.py`

Concatenated transformer for IQ generation: 
`python -u transformer/train_gen_iq_concat.py`

LSTM for MusicNet generation: 
`python -u lstm_music_gen.py`

LSTM for IQ generation: 
`python -u lstm_iq_gen.py`

## Path Configuration
Example: `python -u transformer_train.py --path PATH`
## Parameter Tuning
All the parameters you can tune are in the argparser section of train*.py or lstm*.py file.

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