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Machine Learning for the Birds: Building Your Own Bird Vocalization Classifier
- Scientists utilize automated systems like autonomous recording units (ARUs) to record audio in forest and jungle areas for studying ecosystems and identifying different species of animals and insects.
- Google Research notes the significance of bird vocalizations in understanding food systems and forest health, emphasizing the value of audio-based identification for birds.
- The BirdCLEF+ 2025 competition on Kaggle requires participants to design a classification model to predict bird species from audio recordings, leading to the need for a custom model due to species outside of existing classifiers.
- This guide outlines the creation of a bird vocalization classifier using techniques similar to Google Research's approach, leveraging the BirdCLEF+ 2025 competition dataset for training.
- The training dataset includes 28,564 audio recordings in the train_audio directory, representing various bird species, with taxonomy details provided in the taxonomy.csv file.
- The competition dataset involves 206 bird species, with 63 classes not covered by the GBV classifier, leading to imbalance and quality issues in some classes.
- Training audio recordings often contain human speech annotations, and tactics to address class imbalance and annotation challenges are discussed in the classifier building section.
- The classifier design involves splitting audio, converting to mel spectrograms, and training an EfficientNet B0 model, leveraging pre-trained models from Google Research.
- To address data imbalance and human annotations, the approach includes pseudo-labeling soundscapes data, augmenting minority classes, and generating mel spectrograms for model input.
- Training results show overfitting with accuracy above 90% but fluctuating validation accuracy, indicating room for improvement in generalization.
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