Neural Networks with Dilated Convolutions for Sound Event Recognition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140671" target="_blank" >RIV/00216305:26220/21:PU140671 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Neural Networks with Dilated Convolutions for Sound Event Recognition
Original language description
Convolutional neural networks, most commonly deployed in image classification tasks, typically use square-shaped convolutional kernels, which are well suited for feature extraction from two-dimensional data. This study explores the effect of utilizing spectrally aware dilated convolutions specialized for sound event recognition. By extending the base kernels in the time or the frequency dimension, the features extracted from the spectral audio representations should, in theory, better capture the temporal and timbral information of different sound events. The baseline neural network model with squared kernels was compared against three models, which used an increasing dilation factor in the subsequent convolutional layers. The three models were purposefully tuned to focus towards the frequency and time feature extraction. The results have shown that the models with dilated convolutions performed noticeably better in comparison with the baseline model.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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