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Update project roadmap and documentation

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- Reflect current implementation status with complete GUI and font system
- Update roadmap with prioritized development plan focusing on plot control widgets
- Reorganize future goals into urgent/short-term/mid-long term categories
- Add comprehensive feature overview and usage instructions
- Clarify next priority: plot control system clustering

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Mikkeli Matlock
2025-08-22 00:26:27 +09:00
parent 265e8254cd
commit 9e65e721d4
2 changed files with 159 additions and 82 deletions
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CLAUDE.md
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@@ -5,98 +5,120 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
## Current Implementation
### Core Features
- **Audio Analysis**: Uses librosa to analyze audio files (MP3/WAV support)
- **Audio Analysis**: Uses librosa to analyze audio files (MP3/WAV/FLAC support)
- **Power Visualization**: Generates colorized power magnitude graphs over time
- **Metadata Extraction**: Reads ID3 tags from MP3 files for better file identification
- **GUI Foundation**: Basic PyQt5 drag-and-drop interface (work in progress)
- **Modular GUI Architecture**: Complete PyQt5 interface with drag-and-drop and file dialog support
- **Font Management**: Comprehensive CJK-compatible font system with user-provided font support
- **Threading & Logging**: Robust background processing with detailed logging system
### Technical Stack
- **Audio Processing**: librosa, numpy
- **Visualization**: matplotlib with custom colormaps
- **GUI Framework**: PyQt5 (drag-and-drop functionality)
- **Metadata**: mutagen for MP3 tag reading
- **Visualization**: matplotlib with custom colormaps and embedded Qt widgets
- **GUI Framework**: PyQt5 with modular widget architecture
- **Metadata**: mutagen for audio tag reading
- **Font Support**: Custom font loading system with CJK fallback
### Key Components
#### `master_core.py`
- `AudioFile` class: Main audio processing class
- Loads audio files and extracts basic metrics (max/avg amplitude, BPM)
- `get_energy_levels_over_time()`: Calculates RMS power over rolling windows
- `plot_energy_levels_over_time()`: Creates colorized power graphs with automatic headroom detection
- `analyze_track_librosa()`: Legacy analysis function (dBFS calculations)
- File processing from `files.txt` configuration
#### `main.py`
- PyQt5 drag-and-drop interface
- Currently displays file paths but doesn't integrate with analysis functions
- Placeholder for GUI integration
- Complete GUI application with modular architecture
- Drag-and-drop and file dialog support for audio files
- Integrated font control system
- Real-time analysis display and file management
#### `files.txt`
- Configuration file listing audio files to analyze
- Supports comments (`;` and `#` prefixed lines)
- Currently contains various music file paths
#### `analysis_results_manager.py`
- Background threading for audio analysis
- Results caching and management
- Progress tracking and error handling
#### `audio_visualization_widget.py`
- Embedded matplotlib visualization with Qt integration
- Real-time plot updates and status display
#### `font_control_widget.py` & `font_manager.py`
- Unified font control system with clustered interface
- Auto-detection of custom fonts from `fonts/` directory
- System font discovery and CJK compatibility
- Auto-regeneration of plots when fonts change
#### `master_core.py`
- Core audio analysis functionality
- `AudioFile` class with comprehensive metrics extraction
- RMS power analysis and BPM detection
### Current Analysis Features
- **RMS Power Analysis**: 10-second rolling window with 2-second hops
- **Adaptive Color Mapping**: Automatically adjusts scale based on detected headroom
- High dynamic range: 0-0.6 scale for loud masters
- High dynamic range: 0-0.6 scale for loud masters
- Conservative mastering: 0-0.3 scale for quiet masters
- **BPM Detection**: Automatic tempo analysis
- **Metadata Display**: Artist and title from ID3 tags
- **Metadata Display**: Artist and title from audio tags
- **Real-time Visualization**: Embedded matplotlib plots with font-aware rendering
### Known Issues
- GUI integration incomplete (drag-drop doesn't trigger analysis)
- MP3 tag reading temporarily disabled in some parts
- No interactive features yet implemented
### GUI Features
- **File Management**: Drag-and-drop and file dialog for audio selection
- **Font Control**: Unified font selector with size control and plot regeneration
- **Analysis Display**: Real-time visualization with metadata panels
- **Modular Architecture**: Self-contained widgets for easy layout management
## Future Development Plans
### Short-term Goals
1. **Complete GUI Integration**
- Connect drag-drop functionality to analysis pipeline
- Real-time graph display in GUI window
- File browser for batch processing
### Short-term (Urgent)
1. **Plot Control Widget Cluster**
- Move 'Refresh Plot' into dedicated plot/graph widget cluster
- Add metric selection widget (choose which analysis to display)
- Implement plot style controller (colormap, line vs bar, etc.)
- Prepare foundation for mastering comparison features
2. **Enhanced Metrics**
### Short-term (Not Urgent)
1. **Enhanced Metrics**
- LUFS loudness measurement implementation
- Dynamic range measurement (DR meter)
- Peak-to-average ratio analysis
- Frequency spectrum analysis
- Loudness standards compliance (LUFS)
3. **Interactive Features**
- Zoom/pan on power graphs
- Playback controls with visual cursor
2. **Interactive Plot Features**
- GUI-controllable plotting styles (colormap, visualization type)
- Select axis ranges on the fly with automatic graph updates
- Zoom/pan controls for detailed analysis
- Export analysis results to CSV/JSON
### Medium-term Goals
3. **Advanced GUI Controls**
- Plot style customization interface
- Real-time axis range selection
- Interactive plot manipulation tools
### Mid-Long term (Not Urgent)
1. **Audio Comparison System**
- Reference vs. comparee audio file analysis
- Side-by-side track comparison interface
- A/B testing for mastering versions
- Overlay visualization for comparative analysis
2. **Distribution & Deployment**
- Self-contained executable releases
- Cross-platform packaging
- Installer creation and distribution
### Future Vision
1. **Advanced Analysis Tools**
- Spectral centroid and bandwidth analysis
- Stereo width measurements
- Transient detection and analysis
- Harmonic distortion detection
2. **Comparison Features**
- Side-by-side track comparison
- Reference track overlay
- Mastering version A/B testing
3. **Batch Processing**
- Folder-based analysis
- Automated report generation
- Progress tracking for large collections
### Long-term Vision
1. **VST Plugin Development**
- Real-time analysis during mixing/mastering
- Integration with DAWs
- Live feedback during production
2. **Professional Features**
- EBU R128 compliance checking
- Custom target curves
- Professional reporting formats
- Multi-format export capabilities
3. **VST Plugin Development**
- Real-time analysis during mixing/mastering
- Integration with DAWs
- Live feedback during production
## Development Notes
### Dependencies
@@ -121,12 +143,18 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
## Usage
### Current Usage
1. Run `python main.py` to launch the GUI application
2. Use "Open Audio File..." button or drag-and-drop audio files for analysis
3. Adjust font settings using the Font Settings panel
4. View real-time analysis results with embedded matplotlib plots
5. Select different analyzed files from the file list to compare results
### Legacy Usage (Batch Mode)
1. Add audio file paths to `files.txt`
2. Run `python master_core.py` for batch analysis
3. Run `python main.py` for GUI (incomplete)
### Planned Usage
1. Drag and drop audio files into GUI
2. Real-time analysis with interactive graphs
3. Export reports and comparisons
4. VST plugin for DAW integration
### Planned Usage Enhancements
1. Interactive plot manipulation and style customization
2. LUFS and advanced metric analysis
3. Audio file comparison features
4. Self-contained executable releases
README.md
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# uj-mastering-master
Utility providing metrics to evaluate masterings.
Now boosted by Claude Code.
Custom mastering toolkit providing comprehensive metrics to evaluate audio masterings through visual analysis.
Developed with Claude Code assistance.
## dependencies
librosa, numpy, matplotlib, mutagen
## Features
## usage
### ✅ Current Implementation
- **Complete GUI Application**: Modular PyQt5 interface with drag-and-drop and file dialog support
- **Real-time Analysis**: Background threading with embedded matplotlib visualization
- **Font Management**: CJK-compatible font system with custom font support from `fonts/` directory
- **Audio Support**: MP3, WAV, and FLAC file analysis
- **RMS Power Analysis**: Rolling window analysis with adaptive color mapping
- **Metadata Display**: Automatic extraction and display of audio tags and BPM
### Command Line Analysis
1. Edit `files.txt` to include paths to your audio files (MP3/WAV supported)
- Use `;` or `#` to comment out files
- One file path per line
2. Run: `python master_core.py`
- Generates colorized power magnitude graphs for each file
- Displays BPM and song metadata
- Graphs show RMS power over time with adaptive scaling
### 🚧 In Development
- **Plot Control Widgets**: Dedicated cluster for plot manipulation and style controls
- **LUFS Metrics**: Professional loudness measurement implementation
- **Interactive Plotting**: Real-time axis control and style customization
### GUI Mode (Experimental)
Run: `python main.py`
- Opens drag-and-drop interface
- Currently displays dropped file paths
- Analysis integration coming soon
### 🔮 Planned Features
- **Audio Comparison**: Reference vs. comparee analysis for mastering evaluation
- **Advanced Metrics**: Dynamic range, spectral analysis, and professional standards compliance
- **Standalone Releases**: Self-contained executable distribution
### Output
- Interactive matplotlib graphs showing power levels over time
- Color-coded visualization (autumn colormap)
- Automatic headroom detection and scaling
- Console output with BPM and metadata information
## Dependencies
- **Core**: `librosa`, `numpy`, `matplotlib`, `mutagen`
- **GUI**: `PyQt5`
- **Audio Processing**: Advanced librosa-based analysis pipeline
## Usage
### GUI Application (Recommended)
```bash
python main.py
```
- **Load Files**: Use "Open Audio File..." button or drag-and-drop
- **Font Control**: Adjust interface fonts and regenerate plots automatically
- **Analysis Display**: View real-time RMS power analysis with metadata
- **File Management**: Switch between analyzed files using the file list
### Command Line Analysis (Legacy)
```bash
# 1. Edit files.txt with your audio file paths
# 2. Run batch analysis
python master_core.py
```
## Architecture
### Modular Design
- **Self-contained Widgets**: Easy layout management and customization
- **Background Processing**: Non-blocking analysis with progress feedback
- **Signal-based Communication**: Clean separation between GUI and analysis logic
### Key Components
- `main.py`: Complete GUI application with modular architecture
- `font_control_widget.py`: Unified font management with plot regeneration
- `analysis_results_manager.py`: Threaded analysis with caching
- `audio_visualization_widget.py`: Embedded matplotlib with Qt integration
## Development Roadmap
### 🎯 Next Priority: Plot Control System
Moving from font-focused interface to comprehensive plot manipulation:
- Cluster plot controls (refresh, style, metric selection)
- Interactive axis range selection
- Real-time plot style customization
- Foundation for comparison features
### 🎵 Short-term Goals
- **LUFS Implementation**: Professional loudness standards
- **Plot Interactivity**: GUI-controlled visualization styles
- **Metric Selection**: Choose which analysis to display
### 🎼 Long-term Vision
- **Mastering Comparison**: Side-by-side analysis tools
- **Professional Standards**: EBU R128 compliance checking
- **Standalone Distribution**: Self-contained executable releases