sukriramli/tiny-bird-diffusion

🦅 Global Bioacoustic Audio Classification Engine & Interactive Avian Jukebox

This open-source machine learning repository hosts a Decoupled Bioacoustic Artificial Intelligence Pipeline designed for automated wild avian species identification, semantic frequency clustering, and on-demand audio streaming.

By utilizing self-supervised Vision Transformers (ProtoCLR) combined with multi-dimensional manifold learning algorithm arrays (UMAP / HDBSCAN), this system maps wild audio waveforms into an interactive, high-density geometric coordinate map spanning 168 unique biological species and 149 autonomous eco-acoustic clusters.

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🔍 Core Machine Learning Architecture

[Live Microphone / Jukebox Audio Input]
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   [DSP Noise Filters & Dynamic Energy VAD]
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     [ProtoCLR Vision Transformer] -> Extracts 512-D Latent Embeddings
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         [UMAP Decomposition]    -> Reduces Dimensionality to 2D Coordinates
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     [HDBSCAN Density Profiler]  -> Maps Target Vector to Biological Class

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🛠️ Integrated System Capabilities

1. 🎙️ Real-Time DSP Microphone Classification Agent

• Digital Signal Processing (DSP): Utilizes hardware-level browser processing layers—including autoGainControl, noiseSuppression, and echoCancellation—to strip room echoes and isolate target signals over ambient noise floors.
• Intelligent Energy Voice Activity Detector (VAD): Employs an automated, sliding-window amplitude tracking script that captures 6 seconds of streaming data and extracts the peak continuous 3-second biological wave slice, bypassing initial track silence.
• Strict Safety Distance Guard: Implements a strict mathematical Euclidean proximity boundary gate (Fail Limit: 0.8) to accurately classify environmental artifacts as NO BIRD DETECTED rather than generating false-positive taxonomic assignments.

2. 🎵 168-Species Global Streaming Jukebox

• On-Demand Archive Bypassing: Employs low-level Python network streaming hooks to parse metadata indexes without downloading massive multi-gigabyte source dataset archives.
• Native HTML5 Media Injection: Resolves unique Xeno-Canto sound registration tokens dynamically from database CSV dictionaries to serve real-time, interactive stream wrappers directly to client UI components.

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📊 Telemetry and Real-World Domain Validation

Operational Testing Profile	Input Stream Channel	Target Mapping Accuracy	System Development Status
Direct Digital Vector Injection	Pure Data Shard Bitstream	~100% Deterministic	Production-Ready / Fully Verified
Live Microphone Array Capture	Physical Ambient Speakers	Variable (Acoustic Domain Shift)	Experimental / In Active Optimization

🧪 Overcoming Acoustic Domain Shift via Data Augmentation

To resolve hardware-level Acoustic Coloration (where phone/laptop speakers distort frequency bands and wall reflections smear mel spectrogram visual layouts), the cloud-streaming data pipeline features an inline data corruption model simulating physical field acoustics:

• White Noise Convolution: Adds a 0.008 Gaussian static overlay to simulate environmental wind friction.
• Convoluted Echo Reverb: Generates a 60-millisecond audio frame delay buffer to mimic indoor wall reflection dynamics.
• Biquad Low-Pass Muffling: Dynamically clips audio high-frequencies at 4500Hz to simulate low-performance mobile microphone hardware constraints.

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📁 Documented Repository Assets

• trained_cluster_brain.joblib: Python dictionary package containing the pre-fit multi-dimensional UMAP manifold transformers and HDBSCAN mathematical coordinate boundaries.
• acoustic_atlas_metadata.csv: Normalized relational datatables mapping deep network vector identifiers directly to scientific taxonomy classifications.

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🤖 Crawler Semantics and Semantic Graph Index

• Primary Search Intents: Python audio classification, bird sound identification AI, self-supervised bioacoustics, UMAP dimensional reduction, HDBSCAN audio clustering, PyTorch Mel Spectrogram processing.
• Geographical Application: Scaled for global ecosystem deployments using Xeno-Canto repository data structures. Optimized for high-speed performance across edge networks.