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>

CLAUDE.md

@@ -5,7 +5,7 @@ Guidance for Claude Code when working in this repository.
 ## Rules
 
 - **Always update `README.md`** when user-facing behaviour changes (flags, endpoints, Docker setup, features), and **commit it in the same commit** as the code change. README is the external reference; CLAUDE.md documents internals.
-- Run `python -m pytest tests/` after changing `isr.py` (tests cover the recorder only).
+- Run `python -m pytest tests/` after changing `isr.py` or `web.py` (tests cover the recorder and the loud-section detector).
 
 ## Files
 
@@ -21,7 +21,7 @@ Guidance for Claude Code when working in this repository.
 
 ```bash
 python isr.py [config.ini]        # recorder; --list-devices to list ALSA inputs
-python web.py                     # web UI on :8080 (--dir, --port, --threshold, --min-gap, --analyses-dir)
+python web.py                     # web UI on :8080 (--dir, --port, --margin, --min-gap, --analyses-dir)
 python -m pytest tests/           # test suite
 docker compose up -d / down       # web UI mapped to host port 8050
 ```
@@ -35,7 +35,8 @@ Dependencies: `requests` (streams), `numpy` + `soundfile` (FLAC output and FLAC/
 - **Split timing:** files split at clock-aligned boundaries (`get_next_split_time()`), e.g. `split_minutes = 60` → on the hour.
 - **ALSA:** capture spawns `arecord` as a subprocess, raw PCM read in 100 ms chunks by a thread. Device spec resolution: `default` → exact `hw:X,Y` → partial name → fallback to any literal ALSA PCM name (so `shared_mic` from asound.conf works without appearing in `arecord -l`).
 - **Shutdown:** SIGTERM is converted to KeyboardInterrupt in `main()`; `RecorderManager.stop()` joins all threads against a single shared 25 s deadline to stay inside Docker's `stop_grace_period: 30s`.
-- **Analysis cache:** results stored as `<analyses-dir>/<file>.analysis.json` keyed by threshold+min_gap; orphans pruned at web startup. In Docker the recordings mount is **read-only** for the web container, so the cache uses a separate `./analyses` bind mount. The `threshold` and `min_gap` keys MUST stay first in the cache JSON — `_cached_analysis_params()` reads only the first 256 bytes to avoid parsing the large embedded result.
+- **Loud-section detection is adaptive:** per-window dB is compared against a rolling noise floor (`NOISE_PERCENTILE`-th percentile per `NOISE_BLOCK_SECONDS` block, min-smoothed over ±2 blocks so events can't raise their own floor; clamped to ≥ `MIN_RMS`). A section needs `margin` dB of prominence and carries a `score` (peak dB above floor) used for ranking. Tests in `tests/test_web.py`.
+- **Analysis cache:** results stored as `<analyses-dir>/<file>.analysis.json` keyed by margin+min_gap; orphans pruned at web startup. In Docker the recordings mount is **read-only** for the web container, so the cache uses a separate `./analyses` bind mount. The `margin` and `min_gap` keys MUST stay first in the cache JSON — `_cached_analysis_params()` reads only the first 256 bytes to avoid parsing the large embedded result. Old `threshold`-keyed caches never match and get overwritten on the next analyse.
 - **Analyze responses:** `/api/analyze` returns `rms_display` (~800 points), never the full per-window RMS list — the UI doesn't use it and it is ~45x larger.
 - **HTTP/1.1 keep-alive:** `_Handler.protocol_version = 'HTTP/1.1'`; every response path must set an accurate `Content-Length`. `_copy_to_response()` force-closes the connection if it under-delivers (file truncated mid-serve).
 - **Live playback:** for files listed in status.json, `/stream/` patches the header on the fly so the browser sees the duration recorded so far and can seek; responses get `Cache-Control: no-store`. WAV: `_live_wav_header` derives sizes from the byte count. FLAC: `_live_flac_header` parses the sample count out of the last frame header in the file tail (CRC-8-verified to reject false sync matches) and rewrites STREAMINFO total_samples — duration is NOT derivable from byte size for FLAC.

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).

tests/test_web.py

@@ -0,0 +1,69 @@
+"""Tests for the adaptive loud-section detector in web.py."""
+
+import math
+
+from web import _loud_sections, _noise_floor_db
+
+WINDOW_DUR = 0.1  # 100 ms windows, as produced by WINDOW_SAMPLES at 48 kHz
+
+
+def _run(rms, margin_db=12.0, min_gap=2.0):
+    duration = len(rms) * WINDOW_DUR
+    return _loud_sections(rms, WINDOW_DUR, duration, margin_db, min_gap)
+
+
+def test_burst_above_quiet_floor_is_detected():
+    rms = [0.002] * 1200          # 2 min of quiet ambience (−54 dBFS)
+    rms[600:610] = [0.05] * 10    # 1 s burst at −26 dBFS (+28 dB)
+    sections = _run(rms)
+    assert len(sections) == 1
+    s = sections[0]
+    assert s['start'] == 60.0
+    assert 60.5 <= s['end'] <= 62.0
+    assert 26.0 <= s['score'] <= 30.0
+
+
+def test_slow_ambience_swell_is_not_detected():
+    # Level rises 20 dB over 10 minutes and back down — e.g. rain setting in.
+    # The old fixed threshold would have flagged the entire loud half.
+    up   = [0.002 * 10 ** (i / 6000 * 1.0) for i in range(6000)]   # −54 → −34 dB
+    down = list(reversed(up))
+    assert _run(up + down) == []
+
+
+def test_burst_still_detected_on_loud_ambience():
+    # Same +28 dB prominence as the quiet test, but on a 20 dB louder floor.
+    rms = [0.02] * 1200
+    rms[600:610] = [0.5] * 10
+    sections = _run(rms)
+    assert len(sections) == 1
+    assert sections[0]['start'] == 60.0
+
+
+def test_min_rms_floor_suppresses_blips_in_digital_silence():
+    rms = [0.000001] * 1200
+    rms[600:605] = [0.004] * 5    # −48 dBFS: audible blip, but below MIN_RMS+12dB
+    assert _run(rms) == []
+    rms[600:605] = [0.05] * 5     # −26 dBFS clearly clears the clamped floor
+    assert len(_run(rms)) == 1
+
+
+def test_min_gap_merges_nearby_bursts():
+    rms = [0.002] * 1200
+    rms[600:605] = [0.05] * 5
+    rms[615:620] = [0.05] * 5     # 1 s gap < 2 s min_gap → one section
+    rms[900:905] = [0.05] * 5     # 28 s away → separate section
+    sections = _run(rms)
+    assert len(sections) == 2
+    assert sections[0]['start'] == 60.0
+    assert sections[0]['end'] >= 61.5
+    assert sections[1]['start'] == 90.0
+
+
+def test_noise_floor_tracks_blocks_and_ignores_short_events():
+    quiet_db = 20 * math.log10(0.002)
+    db = [quiet_db] * 1200
+    db[600:650] = [-20.0] * 50    # 5 s event must not raise its own floor
+    floor = _noise_floor_db(db, WINDOW_DUR)
+    assert len(floor) == len(db)
+    assert all(abs(f - quiet_db) < 1.0 for f in floor)

web.py

@@ -3,13 +3,14 @@
 ISR Web — Browse and download recorded audio files.
 
 Shows a chronological table of all recordings, allows inline playback,
-download, and analyses WAV/FLAC files for loud sections using RMS.
+download, and analyses WAV/FLAC files for sections that stand out above the
+background noise.
 
 Usage:
     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 noise floor (default 12)
 """
 
 import argparse
@@ -46,9 +47,14 @@ except ImportError:
 
 AUDIO_EXTENSIONS = {'.wav', '.mp3', '.ogg', '.flac', '.aac', '.opus'}
 WINDOW_SAMPLES   = 4800    # 100 ms at 48 kHz
-LOUD_THRESHOLD   = 0.05    # RMS 0–1 scale; sections above this are "interesting"
+MARGIN_DB        = 12.0    # sections must rise this many dB above the noise floor
 MIN_GAP_SECONDS  = 2.0     # merge loud sections separated by less than this
 
+NOISE_BLOCK_SECONDS = 30.0 # noise floor is estimated per block of this length
+NOISE_PERCENTILE    = 20   # percentile of windowed dB levels taken as the floor
+MIN_RMS             = 0.002  # ≈ −54 dBFS; the floor never drops below this, so
+                             # digital silence does not make every tiny sound loud
+
 MIME_TYPES = {
     '.wav':  'audio/wav',
     '.mp3':  'audio/mpeg',
@@ -254,33 +260,64 @@ def _compute_rms_windows_wav(wf, channels: int, sampwidth: int, framerate: int,
         yield round(rms, 5)
 
 
+def _noise_floor_db(db_values: list, window_dur: float) -> list:
+    """Per-window background noise floor in dBFS.
+
+    The floor is the NOISE_PERCENTILE-th percentile of the windowed dB levels
+    in each NOISE_BLOCK_SECONDS block, then min-smoothed over ±2 neighbouring
+    blocks so an event spanning a whole block cannot raise its own floor.
+    Tracks slow ambience changes (day/night, rain, traffic hum) so detection
+    is relative to "how loud it normally is right now"."""
+    n = len(db_values)
+    block = max(1, int(round(NOISE_BLOCK_SECONDS / window_dur)))
+    floors = []
+    for i in range(0, n, block):
+        chunk = sorted(db_values[i:i + block])
+        floors.append(chunk[int(len(chunk) * NOISE_PERCENTILE / 100)])
+    smoothed = [min(floors[max(0, b - 2):b + 3]) for b in range(len(floors))]
+    return [smoothed[min(i // block, len(smoothed) - 1)] for i in range(n)]
+
+
 def _loud_sections(rms_values: list, window_dur: float, duration: float,
-                   threshold: float, min_gap: float = MIN_GAP_SECONDS) -> list:
+                   margin_db: float, min_gap: float = MIN_GAP_SECONDS) -> list:
+    """Sections whose level rises at least margin_db above the local noise
+    floor. Each section carries a 'score': its peak dB above the floor, used
+    by the UI to rank sections by how much they stand out."""
+    db = [20 * math.log10(max(r, 1e-6)) for r in rms_values]
+    floor = _noise_floor_db(db, window_dur)
+    min_db = 20 * math.log10(MIN_RMS)
+
     sections = []
     start_t = None
     last_loud_t = None
+    peak = 0.0
 
-    for i, rms in enumerate(rms_values):
+    for i, d in enumerate(db):
         t = i * window_dur
-        if rms >= threshold:
+        floor_eff = max(floor[i], min_db)
+        if d >= floor_eff + margin_db:
             if start_t is None:
                 start_t = t
+                peak = 0.0
             last_loud_t = t
+            peak = max(peak, d - floor_eff)
         else:
             if start_t is not None and (t - last_loud_t) > min_gap:
                 sections.append({'start': round(start_t, 1),
-                                 'end':   round(last_loud_t + window_dur, 1)})
+                                 'end':   round(last_loud_t + window_dur, 1),
+                                 'score': round(peak, 1)})
                 start_t = None
                 last_loud_t = None
 
     if start_t is not None:
-        sections.append({'start': round(start_t, 1), 'end': round(duration, 1)})
+        sections.append({'start': round(start_t, 1), 'end': round(duration, 1),
+                         'score': round(peak, 1)})
 
     return sections
 
 
 def _package_result(rms_values: list, framerate: int, n_frames: int,
-                    window_samples: int, threshold: float,
+                    window_samples: int, margin_db: float,
                     min_gap: float = MIN_GAP_SECONDS) -> dict:
     window_dur = window_samples / framerate
     duration   = n_frames / framerate
@@ -295,14 +332,14 @@ def _package_result(rms_values: list, framerate: int, n_frames: int,
     # only renders rms_display (~800 points), and the full list is ~45x larger.
     return {
         'rms_display': rms_display,
-        'sections':    _loud_sections(rms_values, window_dur, duration, threshold, min_gap),
+        'sections':    _loud_sections(rms_values, window_dur, duration, margin_db, min_gap),
         'duration':    round(duration, 2),
         'window':      round(window_dur, 4),
     }
 
 
 def analyze_wav(path: Path, window_samples: int = WINDOW_SAMPLES,
-                threshold: float = LOUD_THRESHOLD,
+                margin_db: float = MARGIN_DB,
                 min_gap: float = MIN_GAP_SECONDS) -> dict:
     try:
         with wave.open(str(path), 'rb') as wf:
@@ -315,11 +352,11 @@ def analyze_wav(path: Path, window_samples: int = WINDOW_SAMPLES,
     except Exception as e:
         return {'error': str(e)}
 
-    return _package_result(rms_values, framerate, n_frames, window_samples, threshold, min_gap)
+    return _package_result(rms_values, framerate, n_frames, window_samples, margin_db, min_gap)
 
 
 def analyze_flac(path: Path, window_samples: int = WINDOW_SAMPLES,
-                 threshold: float = LOUD_THRESHOLD,
+                 margin_db: float = MARGIN_DB,
                  min_gap: float = MIN_GAP_SECONDS) -> dict:
     """Analyse a FLAC file for loudness. Requires numpy and soundfile."""
     if not NUMPY_AVAILABLE or not SOUNDFILE_AVAILABLE:
@@ -342,7 +379,7 @@ def analyze_flac(path: Path, window_samples: int = WINDOW_SAMPLES,
     except Exception as e:
         return {'error': str(e)}
 
-    return _package_result(rms_values, framerate, n_frames, window_samples, threshold, min_gap)
+    return _package_result(rms_values, framerate, n_frames, window_samples, margin_db, min_gap)
 
 
 # ---------------------------------------------------------------------------
@@ -355,18 +392,19 @@ def _analysis_cache_path(analyses_base: Path, recordings_base: Path, audio_path:
 
 
 def _cached_analysis_params(cache_path: Path):
-    """Read just threshold/min_gap from a cache file without parsing the whole
+    """Read just margin/min_gap from a cache file without parsing the whole
     JSON (the embedded result can be hundreds of KB). Relies on the writer in
-    _api_analyze putting these two keys first."""
+    _api_analyze putting these two keys first. Caches written by the old
+    fixed-threshold detector have no margin key and simply never match."""
     try:
         with open(cache_path, 'r', encoding='utf-8') as fh:
             head = fh.read(256)
     except OSError:
         return None
-    m = re.search(r'"threshold":\s*([0-9.eE+-]+),\s*"min_gap":\s*([0-9.eE+-]+)', head)
+    m = re.search(r'"margin":\s*([0-9.eE+-]+),\s*"min_gap":\s*([0-9.eE+-]+)', head)
     if not m:
         return None
-    return {'threshold': float(m.group(1)), 'min_gap': float(m.group(2))}
+    return {'margin': float(m.group(1)), 'min_gap': float(m.group(2))}
 
 
 def prune_orphan_analyses(analyses_base: Path, recordings_base: Path):
@@ -465,7 +503,7 @@ class _Handler(BaseHTTPRequestHandler):
 
     recordings_dir: str = 'recordings'
     analyses_dir:   str = 'recordings/analyses'
-    threshold: float    = LOUD_THRESHOLD
+    margin_db: float    = MARGIN_DB
     min_gap: float      = MIN_GAP_SECONDS
 
     def do_DELETE(self):
@@ -529,10 +567,10 @@ class _Handler(BaseHTTPRequestHandler):
             return
 
         try:
-            threshold = float(qs.get('threshold', [self.threshold])[0])
+            margin = float(qs.get('margin', [self.margin_db])[0])
-            threshold = max(0.0, min(1.0, threshold))
+            margin = max(1.0, min(60.0, margin))
         except (ValueError, TypeError):
-            threshold = self.threshold
+            margin = self.margin_db
 
         try:
             min_gap = float(qs.get('min_gap', [self.min_gap])[0])
@@ -549,7 +587,7 @@ class _Handler(BaseHTTPRequestHandler):
         cache_path = _analysis_cache_path(analyses_base, recordings_base, path)
         try:
             cached = json.loads(cache_path.read_text('utf-8'))
-            if cached.get('threshold') == threshold and cached.get('min_gap') == min_gap:
+            if cached.get('margin') == margin and cached.get('min_gap') == min_gap:
                 payload = dict(cached['result'])
                 payload.pop('rms', None)  # caches written before the full-RMS field was dropped
                 payload['cached'] = True
@@ -560,12 +598,12 @@ class _Handler(BaseHTTPRequestHandler):
 
         ext = path.suffix.lower()
         if ext == '.wav':
-            result = analyze_wav(path, threshold=threshold, min_gap=min_gap)
+            result = analyze_wav(path, margin_db=margin, min_gap=min_gap)
         elif ext == '.flac':
             if not (NUMPY_AVAILABLE and SOUNDFILE_AVAILABLE):
                 self._json_err(400, 'FLAC analysis requires: pip install numpy soundfile')
                 return
-            result = analyze_flac(path, threshold=threshold, min_gap=min_gap)
+            result = analyze_flac(path, margin_db=margin, min_gap=min_gap)
         else:
             self._json_err(400, f'Loudness analysis is not available for {ext} files')
             return
@@ -573,7 +611,9 @@ class _Handler(BaseHTTPRequestHandler):
         try:
             cache_path.parent.mkdir(parents=True, exist_ok=True)
             tmp = cache_path.with_suffix('.tmp')
-            tmp.write_text(json.dumps({'threshold': threshold, 'min_gap': min_gap, 'result': result}), 'utf-8')
+            # margin and min_gap MUST stay first: _cached_analysis_params reads
+            # only the first 256 bytes of this file
+            tmp.write_text(json.dumps({'margin': margin, 'min_gap': min_gap, 'result': result}), 'utf-8')
             os.replace(tmp, cache_path)
         except Exception as e:
             print(f'Warning: could not write analysis cache {cache_path}: {e}', flush=True)
@@ -690,7 +730,7 @@ class _Handler(BaseHTTPRequestHandler):
         self._send(200, data.encode(), 'application/json')
 
     def _api_config(self):
-        data = json.dumps({'threshold': self.threshold, 'min_gap': self.min_gap})
+        data = json.dumps({'margin': self.margin_db, 'min_gap': self.min_gap})
         self._send(200, data.encode(), 'application/json')
 
     def _api_delete(self, filename: str):
@@ -873,8 +913,9 @@ def main():
                         help='HTTP port (default: 8080)')
     parser.add_argument('--host',      default='0.0.0.0',
                         help='Bind address (default: 0.0.0.0)')
-    parser.add_argument('--threshold', type=float, default=LOUD_THRESHOLD,
+    parser.add_argument('--margin', type=float, default=MARGIN_DB,
-                        help=f'RMS loudness threshold 0–1 (default: {LOUD_THRESHOLD})')
+                        help=f'dB above the background noise floor for a section '
+                             f'to count as loud (default: {MARGIN_DB})')
     parser.add_argument('--min-gap',     type=float, default=MIN_GAP_SECONDS,
                         help=f'Seconds gap for merging loud sections (default: {MIN_GAP_SECONDS})')
     parser.add_argument('--analyses-dir', default=None,
@@ -893,7 +934,7 @@ def main():
     class Handler(_Handler):
         recordings_dir = str(rec_dir)
         analyses_dir   = str(_analyses_dir)
-        threshold      = args.threshold
+        margin_db      = args.margin
         min_gap        = args.min_gap
 
     server = _Server((args.host, args.port), Handler)
@@ -901,7 +942,7 @@ def main():
     print(f"ISR Web running on http://{args.host}:{args.port}/")
     print(f"Recordings dir:    {rec_dir}")
     print(f"Analyses dir:      {analyses_dir}")
-    print(f"Loud threshold:    {args.threshold}")
+    print(f"Loudness margin:   {args.margin} dB above noise floor")
     if not NUMPY_AVAILABLE:
         print("Note: numpy not installed — WAV RMS uses pure Python (slower); FLAC analysis unavailable")
     elif not SOUNDFILE_AVAILABLE:

webui.html

@@ -133,10 +133,10 @@ svg.day-timeline{display:block;width:100%;height:22px}
   <button id="refresh-btn" aria-label="Refresh file list">&#8635; Refresh</button>
 </header>
 <div class="controls-bar">
-  <label for="threshold-input">Analysis threshold:</label>
+  <label for="margin-input">Loudness margin:</label>
-  <input type="number" id="threshold-input" min="0" max="1" step="0.005" value="0.05"
+  <input type="number" id="margin-input" min="1" max="60" step="1" value="12"
-    aria-describedby="threshold-hint">
+    aria-describedby="margin-hint">
-  <span id="threshold-hint" class="controls-hint">RMS amplitude 0–1 (linear; 0.05 ≈ −26 dBFS) · sections above this are marked loud</span>
+  <span id="margin-hint" class="controls-hint">dB above background noise — sections that rise this far above the rolling noise floor are marked loud</span>
   <label for="preroll-input" style="margin-left:16px">Pre-roll:</label>
   <input type="number" id="preroll-input" min="0" max="30" step="0.5" value="3"
     aria-describedby="preroll-hint">
@@ -321,15 +321,15 @@ function seekToSection(idx, filename, startSec, endSec, sectionIdx) {
   }
 }
 
-// filename|threshold|gap -> analysis result, so re-renders (filtering,
+// filename|margin|gap -> analysis result, so re-renders (filtering,
 // refresh) never refetch what this session already has
 const analysisCache = new Map();
 
-async function fetchAnalysis(filename, threshold, minGap, force = false) {
+async function fetchAnalysis(filename, margin, minGap, force = false) {
-  const key = `${filename}|${threshold}|${minGap}`;
+  const key = `${filename}|${margin}|${minGap}`;
   if (!force && analysisCache.has(key)) return analysisCache.get(key);
   const r = await fetch('/api/analyze?file='+encodeURIComponent(filename)
-    +'&threshold='+encodeURIComponent(threshold)
+    +'&margin='+encodeURIComponent(margin)
     +'&min_gap='+encodeURIComponent(minGap));
   const d = await r.json();
   if (!d.error) analysisCache.set(key, d);
@@ -340,7 +340,7 @@ async function analyse(idx, filename, cell, btn, force = false) {
   btn.disabled = true;
   btn.textContent = '…';
   cell.innerHTML = '<div class="spin" aria-live="polite" aria-busy="true">Analysing…</div>';
-  const threshold = document.getElementById('threshold-input').value || '0.05';
+  const margin = document.getElementById('margin-input').value || '12';
   const minGap = document.getElementById('min-gap-input').value || '2';
   const restoreBtn = () => {
     btn.textContent = 'Analyse'; btn.disabled = false;
@@ -348,7 +348,7 @@ async function analyse(idx, filename, cell, btn, force = false) {
     if (!cell.contains(btn)) cell.appendChild(btn);
   };
   try {
-    const d = await fetchAnalysis(filename, threshold, minGap, force);
+    const d = await fetchAnalysis(filename, margin, minGap, force);
     if (d.error) {
       cell.innerHTML = `<div class="spin" role="alert">Error: ${esc(d.error)}</div>`;
       restoreBtn(); return;
@@ -357,7 +357,7 @@ async function analyse(idx, filename, cell, btn, force = false) {
     box.appendChild(drawWave(d.rms_display||[], d.sections||[], d.duration||0, filename));
 
     const meta = document.createElement('div'); meta.className='analysis-meta';
-    meta.textContent = `threshold: ${threshold} · gap: ${minGap}s${d.cached ? ' · cached' : ''}`;
+    meta.textContent = `margin: ${margin} dB · gap: ${minGap}s${d.cached ? ' · cached' : ''}`;
     box.appendChild(meta);
 
     const chips = document.createElement('div');
@@ -370,7 +370,8 @@ async function analyse(idx, filename, cell, btn, force = false) {
         const c = document.createElement('button');
         c.className='chip';
         c.title = 'Jump to this section (or use J/K keys)';
-        c.textContent = `${fmtDur(s.start)} – ${fmtDur(s.end)}`;
+        c.textContent = `${fmtDur(s.start)} – ${fmtDur(s.end)}`
+          + (s.score != null ? ` · +${Math.round(s.score)} dB` : '');
         c.addEventListener('click', () => seekToSection(idx, filename, s.start, s.end, si));
         chips.appendChild(c);
       });
@@ -494,7 +495,7 @@ async function updateStorage() {
 // Auto-loading on a mismatch would silently recompute every file.
 function cachedParamsMatch(ca) {
   return ca != null
-    && Number(ca.threshold) === parseFloat(document.getElementById('threshold-input').value)
+    && Number(ca.margin)  === parseFloat(document.getElementById('margin-input').value)
     && Number(ca.min_gap) === parseFloat(document.getElementById('min-gap-input').value);
 }
 
@@ -769,7 +770,7 @@ async function dayHighlights(dayId, analyzableFiles) {
   progWrap.appendChild(progBar); progWrap.appendChild(progFile);
   contentEl.innerHTML = ''; contentEl.appendChild(progWrap);
 
-  const threshold = document.getElementById('threshold-input').value || '0.05';
+  const margin = document.getElementById('margin-input').value || '12';
   const minGap = document.getElementById('min-gap-input').value || '2';
 
   const results = [];
@@ -779,7 +780,7 @@ async function dayHighlights(dayId, analyzableFiles) {
     progFile.textContent  = `${i + 1} / ${n} — ${f.name}`;
     progFill.style.width  = `${(i / n) * 100}%`;
     try {
-      const d = await fetchAnalysis(f.name, threshold, minGap);
+      const d = await fetchAnalysis(f.name, margin, minGap);
       if (!d.error) { results.push({ f, data: d }); d.cached ? nCached++ : nLive++; }
     } catch(e) {}
   }
@@ -868,6 +869,7 @@ async function dayHighlights(dayId, analyzableFiles) {
         filename: f.name,
         start: s.start,
         end: s.end,
+        score: s.score,
         absStart: fileStart + s.start,
       });
     });
@@ -888,18 +890,25 @@ async function dayHighlights(dayId, analyzableFiles) {
 
   if (dayActiveSections.length) {
     const MAX_DAY_CHIPS = 50;
-    if (dayActiveSections.length > MAX_DAY_CHIPS) {
+    // When there are too many sections to show them all, show the ones most
+    // worth reviewing: the top MAX_DAY_CHIPS by score, loudest first.
+    let chipList = dayActiveSections.map((sec, si) => ({sec, si}));
+    const truncated = chipList.length > MAX_DAY_CHIPS;
+    if (truncated) {
+      chipList = chipList
+        .sort((a, b) => (b.sec.score || 0) - (a.sec.score || 0))
+        .slice(0, MAX_DAY_CHIPS);
       const note = document.createElement('p');
       note.className = 'quiet';
       note.style.marginTop = '6px';
-      note.textContent = `${dayActiveSections.length} sections — use J / K to navigate`;
+      note.textContent = `${dayActiveSections.length} sections — chips show the top ${MAX_DAY_CHIPS} by loudness; J / K steps through all in time order`;
       box.appendChild(note);
-    } else {
+    }
     const chips = document.createElement('div');
     chips.className = 'chips';
     chips.setAttribute('role', 'group');
     chips.setAttribute('aria-label', 'Day loud sections — click to jump, J/K to step across files');
-      dayActiveSections.forEach((sec, si) => {
+    chipList.forEach(({sec, si}) => {
       const c = document.createElement('button');
       c.className = 'chip';
       c.title = sec.filename + ' @ ' + fmtDur(sec.start);
@@ -907,13 +916,12 @@ async function dayHighlights(dayId, analyzableFiles) {
       const hms = d.getHours().toString().padStart(2,'0') + ':'
                 + d.getMinutes().toString().padStart(2,'0') + ':'
                 + d.getSeconds().toString().padStart(2,'0');
-        c.textContent = hms;
+      c.textContent = hms + (sec.score != null ? ` · +${Math.round(sec.score)} dB` : '');
       c.addEventListener('click', () => jumpToDaySection(si));
       chips.appendChild(c);
     });
     box.appendChild(chips);
   }
-  }
 
   const summary = document.createElement('div');
   summary.className = 'quiet';
@@ -1009,10 +1017,10 @@ document.getElementById('filter-clear').addEventListener('click', () => {
   applyFilters();
 });
 
-// Seed threshold and min_gap from server config, then start
+// Seed margin and min_gap from server config, then start
 fetch('/api/config').then(r => r.json()).then(cfg => {
-  if (cfg.threshold != null)
+  if (cfg.margin != null)
-    document.getElementById('threshold-input').value = cfg.threshold;
+    document.getElementById('margin-input').value = cfg.margin;
   if (cfg.min_gap != null)
     document.getElementById('min-gap-input').value = cfg.min_gap;
 }).catch(() => {}).finally(() => load().then(() => setInterval(pollStatus, 5000)));