science-camp-project for migration birds calls

PROJECT DESCRIPTION

🔶 addressing the current gap in automated tools, where existing models fail to accurately recognize species during migration

approach:

🔷 combine advanced ML models, bioacoustics, and ecological data to push the boundaries of bird migration monitoring

Tasks:

🔶 Preprocessing & Audio Enhancement

• Audio segmentation to isolate bird calls from continuous recordings
• Noise cancellation models (e.g., RNNoise) to reduce non-bird background sounds (wind, grasshoppers, traffic)
• Spectrogram and feature extraction (MFCC, frequency bands) to create standardized input for ML models

🔶 Model Development & Experimentation

• CNN, RNN, and CNN+RNN hybrid models trained on bird-call spectrograms to classify species
• Experimentation with LSTM layers for temporal dependencies, given the time-evolution in call sequences
• Noise-cancelled vs. non-cancelled comparison to understand the impact on model performance
• Bird call clustering: exploring unsupervised techniques (e.g., k-means, DBSCAN) to group calls and find patterns or new, unknown species clusters

🔶 Data Integration & Ecological Insights

• Linking spectrogram data to bird migration databases (e.g., EuroBirdPortal) to identify spatiotemporal migration patterns for specific species
• Developing models for behavioral anomaly detection: identifying unusual patterns in migration timing, call frequency, or flight altitude (This could reveal changes due to climate, habitat loss, or other ecological pressures)
• Predictive models based on migration history, forecasting expected migration windows for each species

🔶 Visualization & Result Analysis

• Visualizing migration patterns based on audio detections and species identifications, showing temporal and geographical trends
• ClearML tracking for model performance metrics: accuracy, precision, recall across species
• Ecological impact analysis: visualization of anomalies in migration behavior (e.g., species appearing earlier/later than expected)

RESULTS:

🔸 New models for bioacoustic analysis: Improving detection accuracy specifically for migration calls, filling a gap in the current state of bioacoustics research.

🔸 Ecological anomaly detection: Leveraging audio and migration databases to detect changes in migration patterns, potentially linking these to environmental shifts.

possible add-ons

Cross-domain insights: Combining bird call data with external databases to uncover new insights into migration routes, timing, and species interactions, providing valuable data for ornithologists and conservationists.

WORKFLOW VISUALIZATION:

link:

WORKING RULES

if you have some result you want to share, switch to your branch and upload it with a comment why you uploading it ( f.e. fixing error or added ___ function)