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Submissions for task 1 of the Detection and Classification of Acoustic Scenes and Events Challenge 2019 (http://dcase.community/challenge2019/task-acoustic-scene-classification)
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README.md
mixup_generator.py
random_eraser.py
task_1A.py
task_1C.py

README.md

dcase2019

Submissions for task 1A and task 1C of the Detection and Classification of Acoustic Scenes and Events Challenge 2019 (http://dcase.community/challenge2019/task-acoustic-scene-classification).

The focus is open-set acoustic scene classification. The system consists of a combination of convolutional neural networks for closed-set identification and deep convolutional autoencoders for outlier detection.

No external data nor pretrained models have been used for our system. All neural networks are implemented with Keras and Tensorflow.

When finding this code helpful, or reusing parts of it, a citation would be appreciated:

@inproceedings{wilkinghoff2019open, title={Open-Set Acoustic Scene Classification with Deep Convolutional Autoencoders}, author={Wilkinghoff, Kevin and Kurth, Frank}, booktitle={Proceedings of the Detection and Classification of Acoustic Scenes and Events Workshop (DCASE)}, publisher={New York University}, pages={258--262}, year={2019} }

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