Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping

• URL: http://arxiv.org/abs/2011.02955v1
• Date: Thu, 5 Nov 2020 16:34:11 GMT
• Title: Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
• Authors: Khaled Koutini, Florian Henkel, Hamid Eghbal-zadeh, Gerhard Widmer
• Abstract summary: We investigate and compare several well-known methods to reduce the number of parameters in neural networks. We show that we can achieve high-performing low-complexity models by applying specific restrictions on the Receptive Field. We propose a filter-damping technique for regularizing the RF of models, without altering their architecture.
• Score: 7.0349768355860895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Neural Networks are known to be very demanding in terms of computing and memory requirements. Due to the ever increasing use of embedded systems and mobile devices with a limited resource budget, designing low-complexity models without sacrificing too much of their predictive performance gained great importance. In this work, we investigate and compare several well-known methods to reduce the number of parameters in neural networks. We further put these into the context of a recent study on the effect of the Receptive Field (RF) on a model's performance, and empirically show that we can achieve high-performing low-complexity models by applying specific restrictions on the RFs, in combination with parameter reduction methods. Additionally, we propose a filter-damping technique for regularizing the RF of models, without altering their architecture and changing their parameter counts. We will show that incorporating this technique improves the performance in various low-complexity settings such as pruning and decomposed convolution. Using our proposed filter damping, we achieved the 1st rank at the DCASE-2020 Challenge in the task of Low-Complexity Acoustic Scene Classification.

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