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Rethinking data augmentation for adversarial robustness, , , , , , , and . Information Sciences, (2024)Efficient Training of Audio Transformers with Patchout., , , and . INTERSPEECH, page 2753-2757. ISCA, (2022)Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping., , , and . CoRR, (2020)The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification., , , and . EUSIPCO, page 1-5. IEEE, (2019)Knowledge Distillation from Transformers for Low-Complexity Acoustic Scene Classification., , , and . DCASE, Tampere University, (2022)Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs., , , , and . MediaEval, volume 2670 of CEUR Workshop Proceedings, CEUR-WS.org, (2019)Dynamic Convolutional Neural Networks as Efficient Pre-Trained Audio Models., , and . IEEE ACM Trans. Audio Speech Lang. Process., (2024)Domain Information Control at Inference Time for Acoustic Scene Classification., , , , and . EUSIPCO, page 181-185. IEEE, (2023)Efficient Training of Audio Transformers with Patchout, , , and . (2021)cite arxiv:2110.05069Comment: Submitted to Interspeech 2022. Source code: https://github.com/kkoutini/PaSST.Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping., , , and . DCASE, page 86-90. (2020)