Bird Species Identification from Audio Data
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023
DOI
10.1109/BigDataService58306.2023.00015
First Page
58
Last Page
62
Abstract
Birds are an important indicator species for environmental changes, and identifying bird species can provide valuable insights into changes in their populations and habitats. This research focuses on identifying bird species using audio recordings from the BirdCLEF dataset on Kaggle. Using these audio recordings, we extracted 26 acoustic features like MFCC, spectral centroid, spectral bandwidth, etc. These features were then used to train supervised machine learning models like Decision trees, Random forests, Naive Bayes classifier, Support Vector Classifier (SVC), k-nearest neighbor (K-NN), and Stochastic Gradient Descent (SGD). In the end, we trained the models using the combined features from the original audio and cleaned audio files along with feature selection by performing recursive feature elimination (RFE).
Keywords
Audio classification, Audio signal processing, Bird Song, Machine learning, Supervised classification
Department
Computer Science
Recommended Citation
Ching Seh Wu, Sasanka Kosuru, and Samaikya Tippareddy. "Bird Species Identification from Audio Data" Proceedings - IEEE 9th International Conference on Big Data Computing Service and Applications, BigDataService 2023 (2023): 58-62. https://doi.org/10.1109/BigDataService58306.2023.00015
