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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)Receptive-Field Regularized CNNs for Music Classification and Tagging., , , , , and . CoRR, (2020)On Data Augmentation and Adversarial Risk: An Empirical Analysis., , , , , , , and . CoRR, (2020)Emotion and Theme Recognition in Music with Frequency-Aware RF-Regularized CNNs., , , , and . CoRR, (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)