https://github.com/aisu-programming/Chord-Recognition

Recognize chords in songs using Bidirectional Transformer || AI Cup 2020 - Chord Recognition Competition (9th place) / 和弦辨識競賽 (第九名)
https://github.com/aisu-programming/Chord-Recognition

chord-estimation chord-recognition deep-learning tensorflow transformer

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Recognize chords in songs using Bidirectional Transformer || AI Cup 2020 - Chord Recognition Competition (9th place) / 和弦辨識競賽 (第九名)

• Host: GitHub
• URL: https://github.com/aisu-programming/Chord-Recognition
• Owner: aisu-programming
• License: mit
• Created: 2020-10-21T16:21:28.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2024-04-04T01:21:12.000Z (about 1 year ago)
• Last Synced: 2024-08-05T06:04:32.029Z (10 months ago)
• Topics: chord-estimation, chord-recognition, deep-learning, tensorflow, transformer
• Language: Python
• Homepage: https://aidea-web.tw/topic/43d9cc47-b70e-4751-80d3-a2d7333eb77b
• Size: 246 MB
• Stars: 15
• Watchers: 2
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Chord-Recognition

---

[中文版 README](README_zh-TW.md)

[AI CUP 2020 - Chord Recognition Competition Website](https://aidea-web.tw/topic/43d9cc47-b70e-4751-80d3-a2d7333eb77b)

Awards: https://global.turingcerts.com/en/co/cert?hash=6a1e9c33453834e3eb8f201e558c563868a308f430606bddb863fe89b6638171

---

## Environment

- Hardware：

  - CPU：i7-9700

  - GPU：RTX 2070

- Software：

  - Cuda：cuda_**11.0**.2_451.48_win10

  - cuDNN：cudnn-11.0-windows-x64-v**8.0.4**.30

  - TensorFlow：tensorflow-**2.4**.0

## Development Flow

1. `answerAnalyze.py`:

   Observe training data through CMD.

   

2. `visualization.py`:

   Observe training data much more conveniently by matplotlib.

   

3. `score.py`:

   

   Comprehend the principle of scoring method & implement the scoring program.

   

4. `main.py` → `processData.py`: 

   

   Preprocess the training data into the input data for models.

   

5. `model.py`: 

   

   Start building the fisrt version model.

   

6. `mapping.py`: 

   

   Including all required mapping dictionaries of input data Y for the first version model.

   These mapping dictionaries concludes 544 possible input data Ys in training data -- "CE200".

   

7. `model.py` → `trainModel.py` → `oneFrameModel.py`: 

   

   Modularize models & rename it to one-frame-input predicting model.

   

8. `multiFrameModel.py`: 

   

   Start building the second version model, which can use multiple frames as input data X.

   

9. Improve `processData.py`:

   

   Divide input data X during preprocessing, which significantly improved the accuracy.

> p.s.

> So far, I always input "random 40% part of one song" to predict "answers of the rest 60% part of the song".

> The oneFrameModel's accuracy is about 80%, and the multiFrameModel can achieve 99.9%.

> I then realized that this is an incorrect predicting method in the next step.

10. `multiFrameModel-2.py` → `splitDataModel.py`:

    Extends the conception of multiple-frame input & dividing input data X.

    Changing predicting pattern from "60% for a song -> answers of the rest 40%" to "60% songs -> the rest 40% songs".

    Ex: There are 20 songs in CE200_sample, so I will choose 8 songs as training data, and the rest 12 songs are for validation data. 

    After changing, the accuracy drops to 45%.

    

11. Improve `mapping.py`:

    

    Adopt new mapping dictionary which only contains scoring data Ys.

    Accuracy raise from 45% to 50%.

    

12. `fasterReadingModel.py`：

    

    Adjust the time of preprocessing to the instant before inputting data X.

    This significantly reduce the time for data reading & the GPU's RAM usage.

    

## Result

- Got the 9th place.

- The 1st place used the model in [this paper](https://paperswithcode.com/paper/feature-learning-for-chord-recognition-the).

- The second place used the same model with me, but more preprocess & Post-processing.

- Review: It seems like that information in data are much more important than the architecture of the model. Maybe I should do feature extraction on my own in the next time.