Integrate Honest CNN Classifier with Advanced System

[adhit-r]

Overview

Integrate the working honest CNN classifier with the advanced raga detection system to create a hybrid approach.

Current Status

✅ Honest CNN Classifier - Working system with 50% accuracy on small dataset
✅ Advanced Raga Detection System - Comprehensive cultural knowledge framework
�� Integration Needed - Combine both approaches for optimal performance

Integration Strategy

• Use honest CNN as baseline/fallback system
• Integrate advanced cultural knowledge with CNN architecture
• Implement ensemble approach combining both systems
• Add cultural context to CNN predictions
• Create hybrid confidence scoring

Technical Tasks

• Modify honest CNN to accept cultural features
• Add shruti analysis to CNN input features
• Implement gamaka detection in CNN pipeline
• Create ensemble prediction logic
• Add cultural insights to CNN output

Files to Modify

• ml/training/honest_raga_classifier.py
• ml/training/advanced_raga_detector.py
• ml/training/hybrid_classifier.py (new)

Success Criteria

• Hybrid system achieves >60% accuracy on test dataset
• Cultural insights generated for all predictions
• Fallback mechanism works when advanced system fails
• Performance metrics show improvement over individual systems

Priority: High

This builds on our working foundation to create a production-ready system.