Acoustic Scene Classification with Mismatched Recording Devices
Sprache des Titels:
DCASE 2019 Technical Report
This technical report describes CP-JKU Student team?s approach for Task 1 - Subtask B of the DCASE 2019 challenge. In this context, we propose two loss functions for domain adaptation to learn invariant representations given time-aligned recordings. We show that these methods improve the classification performance on our cross-validation, as well as performance on the Kaggle leader board, up to three percentage points compared to our baseline model. Our best scoring submission is an ensemble of eight classifiers.