Acoustic scene classification with reject option based on resnets
Sprache des Titels:
Englisch
Original Kurzfassung:
This technical report describes the submissions from the SAL/CP
JKU team for Task 1 - Subtask C (classification on data that includes
classes not encountered in the training data) of the DCASE-2019
challenge. Our method uses a ResNet variant specifically adapted
to be used along with spectrograms in the context of Acoustic Scene
Classification (ASC). The reject option is based on the logit values
of the same networks. We do not use any of the provided external data sets, and perform data augmentation only with the mixup technique [1]. The result of our experiments is a system that
achieves classification accuracies of up to around 60% on the public Kaggle-Leaderboard. This is an improvement of around 14 percentage points compared to the official DCASE 2019 baseline
Sprache der Kurzfassung:
Englisch
Erscheinungsjahr:
2019
Anzahl der Seiten:
5
Notiz zur Publikation:
Buchtitel: Detection and Classification of Acoustic Scenes and Events 2019
Forschungs-