Enhancing Bird Identification through Signal Processing and Neural Network

Abstract

The bird population is changing dramatically
in the modern era due to a variety of factors,
including human influence, wildfires, rising
temperatures, and other environmental
issues. The bird population and their
behaviours may now be monitored using
machine learning to identify bird species
automatically. Due to the period and efforts
required to manually identify different bird
species, this work offers a mechanically free
machine for identifying bird touches. To do
this, convolutional neural networks are used
instead of more traditional classifiers like
SVM, Random Forest, and SMACPY.
It is now crucial to keep an eye on how
human activity affects the environment, if we
want to save it from suffering long-term
damage. Monitoring biodiversity, population
dynamics, and animal breeding behaviour is
one way to keep an eye on these consequences.
Birds are among the greatest animals to
examine because they're frequently the
groups most susceptible to unforeseen
modifications, like blazes or development.
13% of all bird species, or about 1,370
species, are estimated to be in danger of going
extinct. Despite their abundance, many bird
species are challenging for humans to
recognize. Up until now, experts have tracked
the birds manually, but this is a timeconsuming and ineffective way.