Pipeline for prototyping audio classification algorithms with TF 2
https://github.com/keunwoochoi/kapre
https://arxiv.org/pdf/1706.05781.pdf
For computation of audio transforms from time to frequency domain on the fly
conda create -n audio python=3.7
activate audio
pip install -r requirements.txt
Assuming you have ipykernel installed from your conda environment
ipython kernel install --user --name=audio
conda activate audio
jupyter-notebook
clean.py can be used to preview the signal envelope at a threshold to remove low magnitude data
When you uncomment split_wavs, a clean directory will be created with downsampled mono audio split by delta time
python clean.py
Change model_type to: conv1d, conv2d, lstm
Sample rate and delta time should be the same from clean.py
python train.py
Assuming you have ran all 3 models and saved the images into logs, check notebooks/Plot History.ipynb
TODO
TODO