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Knowledge Distillation from Transformers for Low-Complexity Acoustic Scene Classification.

, , , and . DCASE, Tampere University, (2022)

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Rethinking data augmentation for adversarial robustness., , , , , , , and . Inf. Sci., (January 2024)Efficient Training of Audio Transformers with Patchout., , , and . INTERSPEECH, page 2753-2757. ISCA, (2022)The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification., , , and . EUSIPCO, page 1-5. IEEE, (2019)Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping., , , and . CoRR, (2020)Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation., , and . CoRR, (2022)Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Sets., , and . CoRR, (2023)Efficient Training of Audio Transformers with Patchout., , , and . CoRR, (2021)Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers., , , , , and . CoRR, (2022)Iterative knowledge distillation in R-CNNs for weakly-labeled semi-supervised sound event detection., , and . DCASE, page 173-177. (2018)Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping., , , and . DCASE, page 86-90. (2020)