A Comparison Study to Identify Birds Species Based on Bird Song Signals

Abstract

this paper presents a comparison study in automatically identifying bird species based on bird acoustic signals, using audio files from XENO-CANTO online database. The features including Mel-Frequency Cepstral Coefficients (MFCC), geo-related meta-features, and the integration are compared. The learning classifiers Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Ensemble Learning are examined. Our experimental results show that in the comparison study, ensemble learning using discriminant learner with the integration of MFCC features and geo meta-features obtains the best detection performance.

Physically based learning activities through recreation are assisted by I-Bird Apps in improving identification skills

“…The results show that the i-Bird application can improve student identification skills. There are many ways to improve bird identification skills using various technological applications such as audio signal processing technology and neural networks (Guo & Liu, 2017;Tatar et al, 2023) and acoustic signal processing using audio files from the XENO-CANTO online database (Guo & Liu, 2017).…”

“…However, traditional identification skills have various limitations, including the five senses needing to be more extensive in identifying distant objects and being able to reach objects that are widely spread. In addition, traditional species identification will take a long time and incur quite high costs if it involves many volunteers to carry out identification, with the probability of finding a species being relatively small and the effectiveness being low (Guo & Liu, 2017;Prihatini et al, 2023;Rath et al, 2022;Tatar et al, 2023;Wheeldon et al, 2019). For students, identifying bird species can be done using a biology textbook to find out the various characteristics of a bird species, such as the shape of the beak, the pattern and color of the bird, the shape of the bird's feet, the typical style when flying in the air, the bird's usual sound, and so on; then matched when observing and identifying birds in the field (Prokop & Rodák, 2009).…”