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DNN Transfer Learning Based Non-Linear Feature Extraction for Acoustic Event Classification

机译:基于DNN传递学习的非线性特征提取用于声音事件分类

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Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments.
机译:最近的声事件分类研究集中在训练合适的表示声事件的过滤器上。但是,由于目标事件数据库的可用性有限以及常规过滤器的线性度,仍有提高性能的空间。通过利用深度神经网络(DNN)的非线性建模及其在预先训练的环境之外的学习能力,这封信提出了一种基于DNN的特征提取方案,用于声音事件的分类。利用室内监视环境数据库证明了该方法的有效性和鲁棒性。

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