1 Ocak 2022 Konferans bildirisi Açık Erişim

Sun, Jianyuan; Liu, Xubo; Mei, Xinhao; Zhao, Jinzheng; Plumbley, Mark D.; Kilic, Volkan; Wang, Wenwu

Dublin Core

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  <dc:creator>Sun, Jianyuan</dc:creator>
  <dc:creator>Liu, Xubo</dc:creator>
  <dc:creator>Mei, Xinhao</dc:creator>
  <dc:creator>Zhao, Jinzheng</dc:creator>
  <dc:creator>Plumbley, Mark D.</dc:creator>
  <dc:creator>Kilic, Volkan</dc:creator>
  <dc:creator>Wang, Wenwu</dc:creator>
  <dc:date>2022-01-01</dc:date>
  <dc:description>Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. In this regard, deep learning based approaches have emerged as a useful tool for ASC problems. Conventional approaches to improving the classification accuracy include integrating auxiliary methods such as attention mechanism, pre-trained models and ensemble multiple sub-networks. However, due to the complexity of audio clips captured from different environments, it is difficult to distinguish their categories without using any auxiliary methods for existing deep learning models using only a single classifier. In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF). DNDF combines a fixed number of convolutional layers and a decision forest as the final classifier. The decision forest consists of a fixed number of decision tree classifiers, which have been shown to offer better classification performance than a single classifier in some datasets. In particular, the decision forest differs substantially from traditional random forests as it is stochastic, differentiable, and capable of using the back-propagation to update and learn feature representations in neural network. Experimental results on the DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method improves the ASC performance in terms of classification accuracy and shows competitive performance as compared with state-of-the-art baselines.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/259607</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:259607</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Deep Neural Decision Forest for Acoustic Scene Classification</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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