scholarly journals Bird Species Classification Using Transfer Learning with Multistage Training

2019 ◽  
pp. 28-38 ◽  
Author(s):  
Akash Kumar ◽  
Sourya Dipta Das
Keyword(s):  
Transfer Learning ◽  
Bird Species ◽  
Species Classification

A Deep Learning-Based Transfer Learning Approach for the Bird Species Classification

2021 ◽  
pp. 43-52
Author(s):  
Tejalal Choudhary ◽  
Shubham Gujar ◽  
Kruti Panchal ◽  
Sarvjeet ◽  
Vipul Mishra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Bird Species ◽  
Learning Approach ◽  
Species Classification

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

Bird species classification using spectrograms

2015 ◽  
Author(s):  
Diego Rafael Lucio ◽  
Yandre Maldonado ◽  
Gomes da Costa
Keyword(s):  
Bird Species ◽  
Species Classification

scholarly journals A Randomized Bag-of-Birds Approach to Study Robustness of Automated Audio Based Bird Species Classification

2021 ◽  
Vol 11 (19) ◽  
pp. 9226
Author(s):  
Burooj Ghani ◽  
Sarah Hallerberg
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bird Species ◽  
Classification Performance ◽  
Species Classification ◽  
Ongoing Research ◽  
Number Of Species ◽  
Artificial Neural ◽  
Sound Features

The automatic classification of bird sounds is an ongoing research topic, and several results have been reported for the classification of selected bird species. In this contribution, we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate, in detail, if and how the classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300 by randomly drawing sounds of species from a dataset of 659 bird species taken from the Xeno-Canto database. We observed that the shallow artificial neural network trained on pre-computed sound features was able to classify the bird sounds. The quality of classifications were at least comparable to some previously reported results when the number of species allowed for a direct comparison. The classification performance is evaluated using several common measures, such as the precision, recall, accuracy, mean average precision, and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depended on the individual composition of the subset and varied across 20 randomly drawn subsets.

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