Profiled hot spots on a 4-min track and cut the worst offenders:
- Remove BPM: librosa.beat.beat_track ran on every load (~3.7s) for a number no
better than tapping by hand. Dropped from AudioFile + the metadata panel.
- LUFS short-term: replace 474 per-window pyloudnorm.integrated_loudness calls
with one K-weighting pass (reusing pyloudnorm's own filter coefficients) + a
vectorised sliding mean-square. This is true *ungated* EBU R128 short-term
(the old loop wrongly gated each 3s window). Integrated + LRA still use
pyloudnorm's gated calls. ~3.8s -> ~1.9s.
- PSR: reuse LUFS's short-term series (memoised on the AudioFile) + vectorised
sample-peak. ~3.0s -> ~0.2s.
- True Peak: oversample the whole signal once, then an O(N) running max over
windows instead of per-window resample_poly. Bit-identical to the old loop
(max|diff| 0.0000 dB). ~2.1s -> ~1.1s.
- Crest Factor: peaks via the same O(N) running max (last per-window loop gone).
lufs+psr+true_peak: ~9.2s -> ~3.2s, plus ~3.7s of BPM removed from every load.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- SpectrogramMetric: log-frequency STFT power heatmap over time, magma
colormap, -80 dB floor. Adaptive hop caps time bins at ~4000 so long
tracks stay responsive on redraw; N_FFT=4096 keeps low-freq resolution.
- master_core: load audio at native sample rate (librosa.load sr=None)
instead of librosa's 22050 Hz default, so the full band up to the
file's own nyquist (~22 kHz at 44.1 kHz) is analysed. ~2x heavier on
44.1/48 kHz files, by design.
- metrics: shared _show_axis_extents helper forces each axis's exact
min/max onto the tick list with compact labels (_fmt_tick), so the
true range is always readable -- notably the spectrogram's 22 kHz top,
which otherwise sits unlabelled between log-scale decade ticks. Applied
to all metric renders.
- Docs: README + CLAUDE updated for the new metric, native-rate loading,
and axis-readability behaviour.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Major refactor from popup-based to persistent PyQt5 interface:
- Extract plotting logic from AudioFile class into separate PlottingEngine
- Create AudioVisualizationWidget with embedded matplotlib canvas
- Add AnalysisResultsManager as bridge between processing and GUI
- Replace simple drag-drop widget with professional splitter layout
- Preserve legacy batch processing mode with execution guard
Features:
- Drag-and-drop audio analysis (.mp3/.wav/.flac support)
- File list with metadata display (BPM, amplitudes, track info)
- Persistent visualization area (no more matplotlib popups)
- Multi-file support with click-to-view functionality
- Threading-ready architecture for future background processing
Technical improvements:
- Clean separation of concerns (analysis/visualization/GUI)
- Qt signal-slot communication pattern
- Modular component design ready for multithreading
- Proper import guards prevent legacy code interference
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added CLAUDE.md with detailed project documentation and roadmap
- Enhanced README.md with usage section for command line and GUI modes
- Updated with Claude Code credit
- Improved master_core.py with better song name handling and BPM display
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Mikkeli Matlock