dipika-singhania/audio-classification

Pytorch code for "Rethinking CNN Models for Audio Classification"

Rethinking CNN Models for Audio Classification

This repository contains the PyTorch code for our paper Rethinking CNN Models for Audio Classification. The experiments are conducted on the following three datasets which can be downloaded from the links provided:

1. ESC-50
2. UrbanSound8K
3. GTZAN

Preprocessing

The preprocessing is done separately to save time during the training of the models.

For ESC-50:

python preprocessing/preprocessingESC.py --csv_file /path/to/file.csv --data_dir /path/to/audio_data/ --store_dir /path/to/store_spectrograms/ --sampling_rate 44100

For UrbanSound8K:

python preprocessing/preprocessingUSC.py --csv_file /path/to/csv_file/ --data_dir /path/to/audio_data/ --store_dir /path/to/store_spectrograms/

For GTZAN:

python preprocessing/preprocessingGTZAN.py --data_dir /path/to/audio_data/ --store_dir /path/to/store_spectrograms/ --sampling_rate 22050

Training the Models

The configurations for training the models are provided in the config folder. The sample_config.json explains the details of all the variables in the configurations. The command for training is:

python train.py --config_path /config/your_config.json