Speed up analysis: drop BPM, vectorise loudness/true-peak, share short-term

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>

CLAUDE.md

@@ -82,10 +82,16 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 - Current registry:
   - `RMSPowerMetric` — 10 s rolling RMS with adaptive colour scale
   - `WaveformMetric` — min/max envelope, fixed ±1.1 y-range
-  - `LUFSMetric` — BS.1770 short-term (3 s) + integrated + LRA, via pyloudnorm
-  - `CrestFactorMetric` — 20·log10(peak/RMS) per 1 s window
-  - `PSRMetric` — sample-peak minus short-term LUFS (3 s window)
-  - `TruePeakMetric` — 4× oversampled dBTP via `scipy.signal.resample_poly`
+  - `LUFSMetric` — true (ungated) EBU R128 short-term (3 s) computed via a single
+    K-weighting pass (`_short_term_lufs`, reusing pyloudnorm's filter
+    coefficients) + a vectorised sliding mean-square; integrated + LRA still come
+    from pyloudnorm (one gated call each). ~2× faster than the old per-window loop
+  - `CrestFactorMetric` — 20·log10(peak/RMS) per 1 s window; peaks via O(N) running max
+  - `PSRMetric` — sample-peak minus short-term LUFS (3 s window); reuses
+    `LUFSMetric`'s short-term series (memoised on the `AudioFile`), so PSR is
+    near-free once LUFS is computed
+  - `TruePeakMetric` — 4× oversampled dBTP; the whole signal is oversampled once
+    (`scipy.signal.resample_poly`) then an O(N) running max over windows
   - `SpectrogramMetric` — log-frequency STFT heatmap; adaptive hop caps time
     bins at ~4000, `N_FFT=4096`. Log/linear frequency is a view toggle
 - Drop in new ones (DR, spectral balance) by appending an instance to `METRICS`;
@@ -96,7 +102,9 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
   renderer, applied uniformly to every metric — not per-metric
 
 #### `master_core.py`
-- Defines the `AudioFile` class: librosa loading, rolling RMS power, BPM detection
+- Defines the `AudioFile` class: librosa loading, rolling RMS power. BPM detection
+  was **removed** — `librosa.beat.beat_track` cost ~3.7 s on every load for a
+  number no better than tapping by hand
 - Loads at **native sample rate** (`librosa.load(..., sr=None)`) so the full
   band is preserved — analysis runs ~2× heavier on 44.1/48 kHz files than the
   old 22050 Hz default, by design
@@ -108,11 +116,9 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 - **Adaptive colour mapping**: Automatically adjusts scale based on detected headroom
   - High dynamic range: 0-0.6 scale for loud masters  
   - Conservative mastering: 0-0.3 scale for quiet masters
-- **Loudness metrics**: LUFS (short-term + integrated + LRA), PSR, Crest Factor
+- **Loudness metrics**: LUFS (ungated short-term + gated integrated + LRA), PSR, Crest Factor
 - **Peak analysis**: True Peak (4× oversampled dBTP)
 - **Spectral view**: log-frequency spectrogram heatmap over time
-- **Readable axes**: exact min/max of every axis is always labelled, even on log scale
-- **BPM detection**: Automatic tempo analysis
 - **Metadata display**: Artist and title from audio tags
 - **Real-time visualization**: Embedded matplotlib plots with font-aware rendering
 
@@ -203,8 +209,8 @@ A custom mastering toolkit that provides metrics to evaluate audio masterings th
 ### Dependencies
 - librosa: Audio analysis and feature extraction
 - numpy: Numerical computations
-- scipy: Signal processing (true-peak polyphase oversampling, spectrogram
-  log-frequency resample)
+- scipy: Signal processing (true-peak polyphase oversampling, K-weighting
+  filters, spectrogram log-frequency resample, O(N) running-max via ndimage)
 - pyloudnorm: BS.1770 loudness (LUFS, LRA)
 - pyqtgraph: Interactive plotting (zoom/pan, overlay, lin/log)
 - matplotlib: Colormaps only (consumed by pyqtgraph) + librosa dependency