This repository includes the code for running the experiments reported in
Zinemanas, P.; Rocamora, M.; Miron, M.; Font, F.; Serra, X. An Interpretable Deep Learning Model for Automatic Sound Classification. Electronics 2021, 10, 850. https://doi.org/10.3390/electronics10070850
APNet uses DCASE-models and therefore please follow the recomendations from this library:
We recommend to install DCASE-models in a dedicated virtual environment. For instance, using anaconda:
conda create -n apnet python=3.6
conda activate apnet
For GPU support:
conda install cudatoolkit cudnn
DCASE-models uses SoX for functions related to the datasets. You can install it in your conda environment by:
conda install -c conda-forge sox
Before installing the library, you must install only one of the Tensorflow variants: CPU-only or GPU.
pip install "tensorflow<1.14" # for CPU-only version
pip install "tensorflow-gpu<1.14" # for GPU version
Now please install DCASE-models:
pip install "DCASE-models==0.2.0-rc0"
Install other dependencies:
pip install "mirdata>=0.3.0"
Now you can clone and use APNet:
git clone https://github.com/pzinemanas/APNet.git
cd APNet
cd experiments
python download_datasets.py -d UrbanSound8k
python train.py -m APNet -d UrbanSound8k -f MelSpectrogram -fold fold1
# Repeat for the other folds
python evaluate.py -m APNet -d UrbanSound8k -f MelSpectrogram