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INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH
(International Peer Reviewed,Refereed, Indexed, Citation Open Access Journal)
ISSN: 2321-9939 | ESTD Year: 2013

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Paper Title
Birds Voice Classification using ResNet
Authors
  Tejashri Manohar Mhatre,  Srijita Bhattacharjee

Abstract
An area of interest in environment is monitoring animal populations to better understand their behavior, biodiversity, and population dynamics. Audible animals can be automatically classified by their sounds, and a specifically applicative environmental designator is the bird, as it responds quickly to changes in its environment. The aim of this study is to improve accuracy of bird species classifier by using deep residual network, which is implemented and used as a baseline. Literature survey is done on the traditional audio recognition techniques by various researchers in field of deep learning and others. This has not only brought the challenges of understanding the issues in every step of recognition as well as new advancements to overcome the limitations of the previous technique and make it effortless on the user end.

Keywords- Accuracy, Deep Residual Network, Recognition
Publication Details
Unique Identification Number - IJEDR1804032
Page Number(s) - 168-172
Pubished in - Volume 6 | Issue 4 | November 2018
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  Tejashri Manohar Mhatre,  Srijita Bhattacharjee,   "Birds Voice Classification using ResNet", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.6, Issue 4, pp.168-172, November 2018, Available at :http://www.ijedr.org/papers/IJEDR1804032.pdf
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