Code for the paper: Changhong Wang, Vincent Lostanlen, and Mathieu Lagrange. Explainable Audio Classification of Playing Techniques with Layer-wise Relevance Propoagation, submitted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
git clone https://github.com/changhongw/examod.git
conda env create -f environment.yml
conda activate examod
This work uses the Studio On Line (SOL) dataset (version 0.9HQ). Please contact the dataset creators for version 0.9HQ if you would like to reproduce all the results in our paper.
After downloading the dataset, the SOL-PMT
subset and the meta data can be automatically generated by running
0_data_meta.ipynb
The data split and the corresponding file IDs used in our paper is shown in the SOL-0.9HQ-PMT_meta.csv
file.
python 1_preprocess_feature.py
(feature extraction takes around 0.5 h on one GPU)
-
Train and test network
python 2_classification.py
(classification with the full scattering feature takes around 15 mins on one GPU) -
Check classification result
3_classification_results.ipynb
-
Local evidence map
4_local_relevance_maps.ipynb
-
Class-wise aggregation
5_classwise_aggregation.ipynb
Please feel free to open an issue if you find any bug.