Analysis Module Enhancements

Analysis Module Enhancements

This document describes the major enhancements made to the analysis module.

TrackAnalyzer Enhancements

### 1. Key Detection
- Implemented: Full key detection using Krumhansl-Schmuckler algorithm
- Output: Camelot notation (e.g., "8A", "5B") with confidence score
- Method: Uses chroma features from librosa to match against key profiles
- Benefits: Enables harmonic mixing recommendations

### 2. Advanced Section Detection
- Verse/Chorus Detection: Identifies verse and chorus sections using energy and harmonic similarity
- Improved Breakdown Detection: Better detection of low-energy breakdown sections
- Section Merging: Automatically merges overlapping sections
- Benefits: More accurate transition point recommendations

### 3. Onset Detection
- Feature: Detects musical onsets (note attacks) throughout the track
- Use Case: Precise beat alignment and transition timing
- Implementation: Uses librosa's onset detection

### 4. Harmonic Analysis
- Harmonic/Percussive Separation: Separates harmonic and percussive components
- Harmonic Energy Ratio: Calculates ratio of harmonic to total energy
- Chroma Features: Extracts chroma features over time for key detection
- Benefits: Better understanding of track structure

### 5. Dynamic Range Analysis
- Feature: Calculates dynamic range (peak vs RMS energy difference in dB)
- Use Case: Understanding track loudness characteristics
- Formula: `20 * log10(peak / mean)`

### 6. Enhanced Energy Analysis
- Mean Energy: Average energy level throughout track
- Peak Energy: Maximum energy level
- Energy at Time: Method to get energy at any point in track
- Benefits: Better energy matching for transitions

### 7. Disk Caching
- Implemented: Full disk cache implementation (was TODO)
- File Modification Checking: Invalidates cache if file has been modified
- JSON Storage: Saves analysis results to JSON files
- Benefits: Faster subsequent analyses, persistent cache across sessions

### 8. Progress Callbacks
- Feature: Optional callback function for progress updates
- Use Case: UI feedback during long analyses
- Format: `callback(status_message, progress_0_to_1)`

### 9. Batch Analysis
- Feature: `analyze_batch()` method for analyzing multiple tracks
- Progress Tracking: Reports progress across batch
- Use Case: Analyzing entire playlists or libraries

### 10. Analysis Statistics
- Feature: Tracks cache hits/misses, total analyzed, errors
- Method: `get_stats()` returns statistics dictionary
- Use Case: Performance monitoring and debugging

### 11. Enhanced TrackAnalysis Dataclass
- New Fields:
- `key_confidence`: Confidence score for key detection
- `energy_mean`, `energy_peak`: Energy statistics
- `verses`, `choruses`: Section detection results
- `onsets`: Onset times
- `chroma`: Chroma features over time
- `harmonic_energy`: Harmonic vs percussive ratio
- `dynamic_range`: Dynamic range in dB
- `file_size`, `file_mtime`: File metadata
- New Methods:
- `get_energy_at_time()`: Get energy at specific time
- `get_section_at_time()`: Get section type at specific time

TransitionAdvisor Enhancements

### 1. Text-to-Speech Announcements
- Implemented: Full TTS implementation (was TODO)
- Platform Support:
- macOS: Uses `say` command
- Linux: Uses `espeak` or `festival`
- Windows: Uses SAPI (Windows Speech API)
- Fallback: Prints to console if TTS unavailable
- Benefits: Voice announcements for transition recommendations

### 2. Enhanced AI Recommendations
- Improved Prompt: More detailed context for Gemini AI
- Better Error Handling: Graceful fallback if AI unavailable
- Structured Output: Better parsing of AI responses

Performance Improvements

1. Cache Invalidation: Only re-analyzes if file modified
2. Memory + Disk Cache: Two-tier caching system
3. Progress Reporting: Better user feedback during analysis
4. Batch Processing: Efficient batch analysis support

Backward Compatibility

All enhancements are backward compatible:
- Existing code continues to work
- New fields have default values
- Optional features (progress callbacks, TTS) are opt-in

Usage Examples

Progress Callback

def progress_callback(status, progress):
    print(f"{status}: {progress*100:.0f}%")

analyzer = TrackAnalyzer(
    cache_dir="./cache",
    progress_callback=progress_callback
)

Batch Analysis

tracks = ["track1.mp3", "track2.mp3", "track3.mp3"]
analyses = analyzer.analyze_batch(tracks)

Get Energy at Time

analysis = analyzer.analyze_track("track.mp3")
energy = analysis.get_energy_at_time(60.0)  # Energy at 60 seconds
section = analysis.get_section_at_time(60.0)  # "chorus", "verse", etc.

Statistics

stats = analyzer.get_stats()
print(f"Cache hit rate: {stats['cache_hits'] / (stats['cache_hits'] + stats['cache_misses'])}")

Future Enhancements

Potential future improvements:
- Genre detection using machine learning
- Mood detection
- Danceability score
- Acoustic fingerprinting for duplicate detection
- Real-time analysis streaming
- GPU acceleration for large batch analyses