Original title: Neural Networks With Dilated Convolutions For Sound Event Recognition
Authors: Miklanek, Stepan
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Convolutional neural networks, most commonly deployed in image classification tasks,typically use square-shaped convolutional kernels, which are well suited for feature extraction fromtwo-dimensional data. This study explores the effect of utilizing spectrally aware dilated convolutionsspecialized for sound event recognition. By extending the base kernels in the time or the frequencydimension, the features extracted from the spectral audio representations should, in theory, bettercapture the temporal and timbral information of different sound events. The baseline neural networkmodel with squared kernels was compared against three models, which used an increasing dilationfactor in the subsequent convolutional layers. The three models were purposefully tuned to focustowards the frequency and time feature extraction. The results have shown that the models withdilated convolutions performed noticeably better in comparison with the baseline model.
Keywords: sound event recognition; convolutional neural networks; dilated convolution
Host item entry: Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers, ISBN 978-80-214-5942-7

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200699

Permalink: http://www.nusl.cz/ntk/nusl-447744


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