Journal article Open Access

Baby Cry Classification Using Machine Learning

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

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>P.Ithaya Rani</dc:creator>
  <dc:creator>P.Pavan Kumar</dc:creator>
  <dc:creator>V.Moses Immanuel</dc:creator>
  <dc:description>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'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.</dc:description>
  <dc:source>International Journal of Innovative Science and Research Technology 7(3) 677-681.</dc:source>
  <dc:title>Baby Cry Classification Using Machine Learning</dc:title>
All versions This version
Views 3131
Downloads 2424
Data volume 6.4 MB6.4 MB
Unique views 2828
Unique downloads 2222


Cite as