The demo application is written in C# with .NETCore. As of July 9, 2020, the only version available is for windows 10 64 bits. Versions for Linux are expected to come as a console application.

Installing the application.

The demo app uses AutoML .NET as the default prediction engine and ONNX runtime as the legacy prediction engine to run the exported model created on python and keras and it needs Visual C++ to be installed on the machine that is going to run the app.

Steps.

1. (Optional, only if you are interested on using ONNX runtime) Install Visual C++ from the Microsoft web site
2. Download the application from our Releases
3. Extract the folder and run ChordsDesktop.exe

• Load any .WAV or .MP3 file and it will split your file in different chords
• Play, Pause and Stop audio controls
• Seek to any chord in particular and resume the reproduction from there.
• Ability to change the length of the window for analyzing the chords
• Ability to correct the model prediction
• Retrain the model based on your corrections

NOTE this was tested using the following setup:

uname -mrs
Linux 5.4.72-microsoft-standard-WSL2 x86_64 # WSL2 Ubuntu 20.04

python --version
Python 3.8.5

This program is a university project on the introductory course to artificial intelligence.

You'll need python 3, pip, and virtualenv(optional but recommended) to run the program

1. Clone the repository
2. Go inside the project folder cd Chords.py/Chords.py
3. Create a virtualenv virtualenv -p python3 my_env
4. Activate your environment source my_env/bin/activate (linux) my_env/Scripts/activate.bat (windows)
5. Install the dependencies pip install -r requirements.txt

At this point you have the environment ready to use several entry points.

• python predict.py or python predict.py path/to/song.wav will give you a prediction of the chord present in that sound file, by default it goes to songs/d.wav which is the D chord. It will show a window similar to this one.

• python split.py songs/guitar/about_a_girl.wav is an example of the split.py entry point that takes a longer song, splits it and runs the prediction for each song piece. The results are saved in a filed called spliter_result.txt

  • Example, if you ran python split.py songs/guitar/about_a_girl.wav, then spliter_result.txt would have the following content: em g em g em em em g em em g g em g g g em g g g em em g em em g g em em g g which are the chords that it was able to identify on the song.

• In the file paper.pdf you'll find the final report of this university project with some references added. Currently it is only available in Spanish, you could go and try to use a translator, hope it helps