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dc.contributor.authorKoutini, Khaled
dc.contributor.authorEghbal-zadeh, Hamid
dc.contributor.authorWidmer, Gerhard
dc.date.accessioned2019-10-24T01:50:18Z-
dc.date.available2019-10-24T01:50:18Z-
dc.date.issued2019-10
dc.identifier.citationK. Koutini, H. Eghbal-zadeh & G. Widmer, "Receptive-Field-Regularized CNN Variants for Acoustic Scene Classification", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 124–128, New York University, NY, USA, Oct. 2019en
dc.identifier.urihttp://hdl.handle.net/2451/60742-
dc.description.abstractAcoustic scene classification and related tasks have been dominated by Convolutional Neural Networks (CNNs). Topperforming CNNs use mainly audio spectograms as input and borrow their architectural design primarily from computer vision. A recent study has shown that restricting the receptive field (RF) of CNNs in appropriate ways is crucial for their performance, robustness and generalization in audio tasks. One side effect of restricting the RF of CNNs is that more frequency information is lost. In this paper, we perform a systematic investigation of different RF configuration for various CNN architectures on the DCASE 2019 Task 1.A dataset. Second, we introduce Frequency Aware CNNs to compensate for the lack of frequency information caused by the restricted RF, and experimentally determine if and in what RF ranges they yield additional improvement. The result of these investigations are several well-performing submissions to different tasks in the DCASE 2019 Challenge.en
dc.rightsDistributed under the terms of the Creative Commons Attribution 4.0 International (CC-BY) license.en
dc.titleReceptive-Field-Regularized CNN Variants for Acoustic Scene Classificationen
dc.typeArticleen
dc.identifier.DOIhttps://doi.org/10.33682/cjd9-kc43
dc.description.firstPage124
dc.description.lastPage128
Appears in Collections:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

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