feat: adaptive noise-floor loudness detection with section scoring

Replace the fixed RMS threshold with prominence over a rolling noise
floor (20th percentile per 30s block, min-smoothed so events cannot
raise their own floor, clamped to -54 dBFS). Slow ambience changes such
as rain or daytime traffic hum move the floor instead of flagging
everything; sections now need `margin` dB (default 12) of prominence.

Each section carries a score (peak dB above floor); day-highlight chips
show the top 50 by score when there are too many to list, so the most
striking events are reviewed first.

--threshold is replaced by --margin; analysis caches are now keyed by
margin+min_gap, old threshold-keyed caches never match and are
overwritten on the next analyse. Detector covered by tests/test_web.py.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -150,7 +150,7 @@ strftime codes are substituted at split time. The file extension is added automa
 python web.py                              # serves ./recordings on port 8080
 python web.py --dir /path/to/audio         # custom recordings directory
 python web.py --port 8888                  # custom port
-python web.py --threshold 0.03             # loudness threshold 0–1 (default 0.05)
+python web.py --margin 15                  # dB above background noise for a section to count as loud (default 12)
 python web.py --min-gap 15                 # grace period in seconds for merging loud sections (default 2)
 python web.py --analyses-dir /path/to/dir  # where to store analysis cache files (default: <recordings>/analyses)
 ```
@@ -160,9 +160,9 @@ The browser UI (HTML/CSS/JS) lives in `webui.html`, which `web.py` loads at star
 Shows recordings grouped by day with collapsible sections. Features:
 
 - **Day groups** — recordings are grouped under a collapsible day heading showing date, file count, total duration, and total size. The most recent day is expanded by default; older days start collapsed. Expanded state is preserved across filter changes.
-- **Day highlights** — click **★ Highlights** on any day heading to run loudness analysis across all WAV/FLAC files in that day and display a combined activity timeline SVG. Orange segments show when loud sections occurred relative to the day's time span; blue shows the file extents. Labels show the start, midpoint, and end times.
+- **Day highlights** — click **★ Highlights** on any day heading to run loudness analysis across all WAV/FLAC files in that day and display a combined activity timeline SVG. Orange segments show when loud sections occurred relative to the day's time span; blue shows the file extents. Labels show the start, midpoint, and end times. When a day has more sections than fit as chips, the chips show the top 50 by score (loudest-above-background first) so the most promising events are reviewed first; J/K still steps through all sections in time order.
 - **Inline playback** — collapsible `▶ Play` button per row; audio loads lazily via a seekable `/stream/` endpoint with HTTP Range support. Metadata is fetched immediately so the duration is visible without pressing play.
-- **Waveform analysis** — on demand per file; computes RMS per 100 ms window and highlights loud sections. Supported for WAV and FLAC (FLAC requires `numpy` + `soundfile`). Pure-Python fallback for WAV when numpy is absent. Results are cached in `recordings/analyses/<filename>.analysis.json`; subsequent requests at the same threshold and min-gap settings return instantly without re-reading the audio. The cache file is deleted automatically when the audio file is deleted. Orphaned cache files (audio deleted outside the UI) are pruned on startup.
+- **Waveform analysis** — on demand per file; computes RMS per 100 ms window and marks sections that stand out above the background. Detection is **adaptive**: a rolling noise floor (20th percentile per 30 s block) is estimated across the file, and a section is flagged when the level rises at least *margin* dB (default 12) above that floor. Slow ambience changes — rain setting in, day/night traffic hum — move the floor instead of producing false positives. Each section gets a **score** (its peak dB above the floor) used to rank sections by how much they stand out. Supported for WAV and FLAC (FLAC requires `numpy` + `soundfile`). Pure-Python fallback for WAV when numpy is absent. Results are cached in `recordings/analyses/<filename>.analysis.json`; subsequent requests at the same margin and min-gap settings return instantly without re-reading the audio. The cache file is deleted automatically when the audio file is deleted. Orphaned cache files (audio deleted outside the UI) are pruned on startup.
 - **Grace period** — configurable in the controls bar (default 2 s). Loud sections separated by less than this gap are merged into one. Raise this (e.g. to 15–30 s) when a single event generates many timestamps due to brief quiet gaps within it.
 - **Timestamp jump** — after analysis, click any loud-section chip to seek the player to that position and pre-fill the cut panel. Use **J** / **K** keyboard shortcuts to jump to the previous / next section while audio is playing.
 - **Cut & download** — `✂ Cut` button opens the player row and reveals a cut panel. Enter start and end times in `m:ss` or `h:mm:ss` format and click **↓ Download cut** to receive an ffmpeg-trimmed copy without re-encoding. Requires ffmpeg (included in the Docker image).