This repository contains the PyTorch code for our paper [Uncertainty Calibration for Deep Audio Classifiers] accepted by INTERSPEECH2022. The experiments are conducted on the following two datasets which can be downloaded from the links provided:
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 44100For GTZAN:
python preprocessing/preprocessingGTZAN.py --data_dir /path/to/audio_data/ --store_dir /path/to/store_spectrograms/ --sampling_rate 22050The 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.jsonpython train_with_focal.py --config_path /config/your_config.jsonpython train_with_sngp.py --config_path /config/your_config.json- Our paper accepted by InerSpeech 2022, now available on ArXiv.com, https://arxiv.org/abs/2206.13071
- https://github.com/kimjeyoung/SNGP-BERT-Pytorch
- https://github.com/kamalesh0406/Audio-Classification
- Rethinking CNN Models for Audio Classification. (https://arxiv.org/abs/2007.11154)
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