fix: use mean+std MFCC vectors (40-dim) for better discrimination

Mean-only vectors were too similar across different audio segments,
causing everything to match even at threshold 0.99. Adding std
captures temporal dynamics and makes the similarity scores much
more spread out.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

core/audio_scan.py

@@ -10,10 +10,14 @@ _SR = 22050
 
 
 def _extract_mfcc(path: str, sr: int = _SR) -> np.ndarray:
-    """Load audio from a file and return a mean MFCC vector (20-dim)."""
+    """Load audio from a file and return an MFCC feature vector (40-dim).
+
+    Concatenates mean + std of each coefficient over time.
+    Mean captures average spectral content; std captures dynamics.
+    """
     y, _ = librosa.load(path, sr=sr, mono=True)
     mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=_N_MFCC)
-    return mfcc.mean(axis=1)  # average over time → (20,)
+    return np.concatenate([mfcc.mean(axis=1), mfcc.std(axis=1)])
 
 
 def build_profile(clip_paths: list[str]) -> dict | None:
@@ -95,7 +99,7 @@ def scan_video(
 
         chunk = y[pos : pos + win_samples]
         mfcc = librosa.feature.mfcc(y=chunk, sr=sr, n_mfcc=_N_MFCC)
-        vec = mfcc.mean(axis=1)
+        vec = np.concatenate([mfcc.mean(axis=1), mfcc.std(axis=1)])
 
         if mode == "nearest":
             score = max(