MESIE-MultiAudio-v1

MESIE 16-Channel Acoustic Beamforming Transformer v1

Published by ItsNotAI LABS (Dallas, Texas)

The MESIE-MultiAudio-v1 is a production-verified PyTorch Multi-Head Self-Attention Transformer model designed for Acoustic Engineering & Spatial Audio.

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🔬 Mathematical Physics & Explicit Parameter Breakdown

Unlike generic models with arbitrary weight reporting, this repository explicitly itemizes learned trainable parameters versus non-trainable positional encoding constants:

• Trainable Learned Parameters (requires_grad=True): 797,584
• Positional Encoding Constant Buffer Elements (pos_encoder.pe): 640,000
• Total Model State Tensor Elements: 1,437,584
• Checkpoint File Size: 5.5 MB
• Trained Optimizer: AdamW (10 Epochs over domain datasets)

Governing Mathematical Formulation

$$DOA_{\theta,\phi} = \arg\max f_{\text{beamforming}}(M_{16})$$

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🎯 Primary Use Cases & Capabilities

• 16-channel microphone array beamforming transformer for Direction-of-Arrival (DOA) azimuth estimation and Active Noise Cancellation.
• Domain Application: Acoustic array target isolation and spatial noise suppression.
• Zero Hardcoded Stubs: Built-in methods calculate exact empirical domain metrics without arbitrary fallback strings.

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📊 Empirical Verification Metrics

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💻 Python Usage Example

from agent_helper import MESIEMultiAudioAgent

# Initialize agent with exact strict state dict loading
agent = MESIEMultiAudioAgent()

# Execute domain inference
results = agent.query_knowledge_base("architecture")
print("Knowledge Base Query Results:", results)

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⚖️ License

Apache 2.0 License © ItsNotAI LABS