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TUC Media Computing at BIRDCLEF 2021: Noise augmentation strategies in bird sound classification in combination with DenseNets and ResNets

This research paper presents deep learning techniques for bird recognition to classify 397 species in the BirdCLEF 2021 challenge. The proposed method was inspired by the DCASE2019 audio tagging challenge, which classifies and recognizes different sound events. Data augmentations methods like noise augmentation, spectrogram augmentation techniques are used to avoid overfitting and hence generalize the model. The final solution is based on an ensemble of different backbone models and splitting the dataset based on geographic locations provided in the test set. Furthermore, framewise post-processing predictions are used to identify the bird events. The best results were obtained from 12 model ensembles with a public and private score of 0.6487 and 0.6034, respectively.