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Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification.

, , , , , and . DCASE, page 204-208. (2019)

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Data-Efficient Low-Complexity Acoustic Scene Classification in the DCASE 2024 Challenge., , , , , , and . CoRR, (2024)Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification., , , , , and . DCASE, page 204-208. (2019)Estimated Audio-Caption Correspondences Improve Language-Based Audio Retrieval., , and . CoRR, (2024)Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples., , , and . DCASE, page 170-174. (2020)Improving Natural-Language-Based Audio Retrieval with Transfer Learning and Audio & Text Augmentations., and . DCASE, Tampere University, (2022)On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers., and . EUSIPCO, page 391-395. IEEE, (2023)Improved Zero-Shot Audio Tagging & Classification with Patchout Spectrogram Transformers., and . EUSIPCO, page 410-413. IEEE, (2022)Improving Audio Spectrogram Transformers for Sound Event Detection Through Multi-Stage Training., , , , and . CoRR, (2024)Fusing Audio and Metadata Embeddings Improves Language-based Audio Retrieval., and . CoRR, (2024)Effective Pre-Training of Audio Transformers for Sound Event Detection., , , , , and . CoRR, (2024)