1 Ocak 2022 Konferans bildirisi Açık Erişim

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

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.48623/aperta.259607</identifier>
  <creators>
    <creator>
      <creatorName>Sun, Jianyuan</creatorName>
      <givenName>Jianyuan</givenName>
      <familyName>Sun</familyName>
    </creator>
    <creator>
      <creatorName>Liu, Xubo</creatorName>
      <givenName>Xubo</givenName>
      <familyName>Liu</familyName>
      <affiliation>Univ Surrey, Ctr Vis Speech &amp; Signal Proc CVSSP, Surrey, England</affiliation>
    </creator>
    <creator>
      <creatorName>Mei, Xinhao</creatorName>
      <givenName>Xinhao</givenName>
      <familyName>Mei</familyName>
      <affiliation>Univ Surrey, Ctr Vis Speech &amp; Signal Proc CVSSP, Surrey, England</affiliation>
    </creator>
    <creator>
      <creatorName>Zhao, Jinzheng</creatorName>
      <givenName>Jinzheng</givenName>
      <familyName>Zhao</familyName>
      <affiliation>Univ Surrey, Ctr Vis Speech &amp; Signal Proc CVSSP, Surrey, England</affiliation>
    </creator>
    <creator>
      <creatorName>Plumbley, Mark D.</creatorName>
      <givenName>Mark D.</givenName>
      <familyName>Plumbley</familyName>
      <affiliation>Univ Surrey, Ctr Vis Speech &amp; Signal Proc CVSSP, Surrey, England</affiliation>
    </creator>
    <creator>
      <creatorName>Kilic, Volkan</creatorName>
      <givenName>Volkan</givenName>
      <familyName>Kilic</familyName>
      <affiliation>Izmir Katip Celebi Univ, Dept Elect &amp; Elect Engn, Izmir, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Wang, Wenwu</creatorName>
      <givenName>Wenwu</givenName>
      <familyName>Wang</familyName>
      <affiliation>Univ Surrey, Ctr Vis Speech &amp; Signal Proc CVSSP, Surrey, England</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Deep Neural Decision Forest For Acoustic Scene Classification</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/259607</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.48623/aperta.259606</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">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.</description>
  </descriptions>
</resource>