Journal article Open Access

Baby Cry Classification Using Machine Learning

P.Ithaya Rani; P.Pavan Kumar; V.Moses Immanuel; P.Tharun; P.Rajesh


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  <identifier identifierType="DOI">10.5281/zenodo.6409004</identifier>
  <creators>
    <creator>
      <creatorName>P.Ithaya Rani</creatorName>
    </creator>
    <creator>
      <creatorName>P.Pavan Kumar</creatorName>
    </creator>
    <creator>
      <creatorName>V.Moses Immanuel</creatorName>
    </creator>
    <creator>
      <creatorName>P.Tharun</creatorName>
    </creator>
    <creator>
      <creatorName>P.Rajesh</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Baby Cry Classification Using Machine Learning</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <dates>
    <date dateType="Issued">2022-04-03</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6409004</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.6409003</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;A Cry is a type of correspondence for kids to communicate their sentiments. Child cry can be portrayed by its regular occasional tone and the difference in voice. Through their child&amp;#39;s cry discovery, guardians can screen their child somewhat just in significant conditions. Recognition of a child cry in discourse signals is a urgent advance in applications like remote child observing and it is likewise significant for researchers, who concentrate on the connection between child cry signal examples and other formative boundaries. This investigation of sound acknowledgment includes highlight extraction and arrangement by deciding the sound example. We use MFCC as an element extraction strategy and K-Nearest Neighbor (K-NN) for arrangement. K-Nearest Neighbor (KNN) is a characterization technique that is regularly utilized for sound information. The KNN classifier is displayed to yield extensively better outcomes contrasted with different classifiers.&lt;/p&gt;</description>
  </descriptions>
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