International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 08 Issue: 07 | July 2021
p-ISSN: 2395-0072
www.irjet.net
BIRD SPECIES DETECTION FROM VOICE FEATURE Kavya Hegde1, Bhagyashree V Bhat2, Bhavyashree V Bhat3 1Professor,
Dept. of computer science Engineering, Srinivas Institute of Technology, Karnataka, India Dept. of computer science Engineering, Srinivas Institute of Technology, Karnataka, India 3Student, Dept. of computer science Engineering, Srinivas Institute of Technology, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------2Student,
Abstract - The goal is to find which species of bird is present
presence/absence in a very given sound clip: a detector
in an audio recording using supervised learning. Devising
outputs a zero if none of the target species are detected and
effective algorithms for bird species classification could be a
a 1 otherwise.
preliminary step toward extracting useful ecological data
The user should operate the software that’s Non-
from recordings collected within the field. In this project use
Real time. This paper uses dataset that contains bird songs
SVM (Support vector machine) algorithm to classify bird
collected from kaggle. The fundamental methodology is
voices into different species supported 256 features extracted
Support vector machine (SVM) is extremely preferred by
from the chipping sound of birds.
many because it produces significant accuracy with less
The challenges during this project included memory
computation power. SVM may be used for both regression
management, the quality of bird species for the machine
and classification tasks. But, its widely utilized in
recognize, and also the mismatch in signal/noise between the
classification objectives.
training and also the testing sets. So to unravel this challenges used SVM algorithm and got good accuracy in it. Here SVM is
The next four section are explained as follows. The section 2
that the best algorithm to resolve the challenges within the
presents related research studies and briefly describes the
recognition. The algorithm SVM got 98.2% accuracy.
bird species recognition problem; Section 3 outlines the
Key Words: supervised learning, support vector machine,
database employed in the popularity experiments and
signal, ecological data, testing set
describes
initial
signal
pre-processing,
syllable
segmentation, the feature extraction procedures and indicate
1.INTRODUCTION
the classification algorithm; Section 4 presents the results
Monitoring birds by their sound is very important for several
obtained in experiments; finally, Section 5 presents
environmental and scientific purposes. A range of
conclusions and indicates future research directions.
crowdsourcing and remote monitoring projects now record
2.RELATED WORKS
these sounds and a few analyses the sound automatically.
There are many related systems are available. These systems
The audio modality is well-suited to bird monitoring because
use different approaches, such as CF, CBF, and hybrid to
many birds are way more clearly detectable by sound than
recommend the popular items. These approaches are
by vision or other indicators. Overview the techniques used
discussed as follows:
for bird species detection, and specific issues to be addressed. Then describe a knowledge challenge which
Lopes, M. T., Silla Junior, C. N., Koerich, A. L., & Kaestner [2]
introducing, with new public datasets, as an initiative to
This paper deals with the automated bird species
advance the state of the art. First, though, must outline the
identification problem, during which its necessary to spot
applications that bird detection in audio is beneficial. The
the species of a bird from its audio recorded song. This can
foremost
be a resourceful thanks to monitor biodiversity in
basic
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