CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification

2020 ◽  
Author(s):  
Tapabrata Chakraborti ◽  
Brendan McCane ◽  
Steven Mills ◽  
Umapada Pal
Keyword(s):  
Bird Species ◽  
Species Classification ◽  
Convolutional Network ◽  
Fine Grained

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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).

scholarly journals Aspect-level sentiment analysis merged with knowledge graph and graph convolutional neural network

2021 ◽  
Vol 2083 (4) ◽  
pp. 042044
Author(s):  
Zuhua Dai ◽  
Yuanyuan Liu ◽  
Shilong Di ◽  
Qi Fan
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Structural Information ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Text Data ◽  
Short Text ◽  
Fine Grained ◽  
Syntactic Information ◽  
Text Information

Abstract Aspect level sentiment analysis belongs to fine-grained sentiment analysis, w hich has caused extensive research in academic circles in recent years. For this task, th e recurrent neural network (RNN) model is usually used for feature extraction, but the model cannot effectively obtain the structural information of the text. Recent studies h ave begun to use the graph convolutional network (GCN) to model the syntactic depen dency tree of the text to solve this problem. For short text data, the text information is not enough to accurately determine the emotional polarity of the aspect words, and the knowledge graph is not effectively used as external knowledge that can enrich the sem antic information. In order to solve the above problems, this paper proposes a graph co nvolutional neural network (GCN) model that can process syntactic information, know ledge graphs and text semantic information. The model works on the “syntax-knowled ge” graph to extract syntactic information and common sense information at the same t ime. Compared with the latest model, the model in this paper can effectively improve t he accuracy of aspect-level sentiment classification on two datasets.

Comparing disturbance-sensitivity between plants and birds: a fine-grained analysis in a suburban remnant wetland

2014 ◽  
Vol 60 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Giuliano Fanelli ◽  
Corrado Battisti
Keyword(s):  
Anthropogenic Disturbance ◽  
Bird Species ◽  
Breeding Birds ◽  
Similar Distribution ◽  
Point Count ◽  
Fine Grained ◽  
Vegetation Disturbance ◽  
Wintering Birds ◽  
The Mean ◽  
Point Count Method

Hemeroby is a concept widely employed in assessment of the effect of human activities on vegetation. In this study, we apply the concept to a set of bird species occurring in a Mediterranean remnant wetland. The aim was to obtain an average hemeroby index for two seasonally related bird assemblages (i.e. breeding and wintering) based on the information related to two levels of plant hemeroby. In a grid of 47 cells 100×100 m-wide, we sampled the fine-grained distribution of plant communities (Braun-Blanquet method/cell) in parallel with birds (point count method; one point count/cell), assigning an independent score of hemeroby to plants and birds on a scale from I to V, from pristine habitats with a lack of natural and/or anthropogenic disturbance (score = I) to completely artificial habitats (score = V). Whereas bird species ranged from categories II to V, vegetation types spanned only the categories III and IV. Therefore, bird species showed a higher variability in hemeroby. By comparing hemeroby scores, we can deduce the effect that the vegetation disturbance may have on bird species. The mean hemeroby for breeding birds, calculated on all the species occurring in a determined plant hemeroby category, is not significantly different between sites with higher (= IV) and lower (= III) plant hemeroby (i.e. higher and lower level of disturbance). The mean hemeroby of the wintering birds was significantly different in the two levels of plant hemeroby (i.e. higher vs. lower hemeroby). Our data suggest that only the wintering birds had a hemeroby distribution pattern related to that of the plants, while the distribution of breeding birds showed no association, i.e. they appear in similar distribution in both plant hemeroby classes. This pattern may reflect the characteristics of the habitat types in relation to bird seasonality: a large section of wintering bird species are strictly water-related, linked to habitats with low plant hemeroby, so appearing more sensitive to change in plant hemeroby when compared to breeding species. Although explorative, our data may be useful in wildlife management, implying that in wetland–grassland mosaics the more sensitive wintering bird species are suitable as indicators aimed to test the effect of natural and anthropogenic disturbances.

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