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Low-Complexity Acoustic Scene Classification for Multi-Device Audio: Analysis of DCASE 2021 Challenge Systems.

, , , and . DCASE, page 85-89. (2021)

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On the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification., , , , and . CoRR, (2019)A case study on feature sensitivity for audio event classification using support vector machines., , and . MLSP, page 1-6. IEEE, (2016)Low-Complexity Acoustic Scene Classification in DCASE 2022 Challenge., , , , , , , and . DCASE, Tampere University, (2022)Adaptive Distance-Based Pooling in Convolutional Neural Networks for Audio Event Classification., , and . IEEE ACM Trans. Audio Speech Lang. Process., (2020)Crowdsourcing Strong Labels for Sound Event Detection., , and . WASPAA, page 246-250. IEEE, (2021)Strong Labeling of Sound Events Using Crowdsourced Weak Labels and Annotator Competence Estimation., and . IEEE ACM Trans. Audio Speech Lang. Process., (2023)Diversity and Bias in Audio Captioning Datasets., and . DCASE, page 90-94. (2021)A Summarization Approach to Evaluating Audio Captioning., , and . DCASE, Tampere University, (2022)Sound Event Envelope Estimation in Polyphonic Mixtures., , , , , and . ICASSP, page 935-939. IEEE, (2019)Analysis of data fusion techniques for multi-microphone audio event detection in adverse environments., , and . MMSP, page 1-6. IEEE, (2017)