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>

tests/test_audio_scan.py

@@ -16,7 +16,7 @@ def test_extract_mfcc_returns_1d_vector():
         _make_wav(f.name)
     try:
         vec = _extract_mfcc(f.name)
-        assert vec.shape == (20,)
+        assert vec.shape == (40,)
         assert not np.isnan(vec).any()
     finally:
         os.unlink(f.name)
@@ -29,7 +29,7 @@ def test_build_profile_single_clip():
         profile = build_profile([f.name])
         assert "mean_vector" in profile
         assert "clip_vectors" in profile
-        assert profile["mean_vector"].shape == (20,)
+        assert profile["mean_vector"].shape == (40,)
         assert len(profile["clip_vectors"]) == 1
     finally:
         os.unlink(f.name)
@@ -49,7 +49,7 @@ def test_build_profile_multiple_clips():
 
         profile = build_profile(paths)
         assert len(profile["clip_vectors"]) == 3
-        assert profile["mean_vector"].shape == (20,)
+        assert profile["mean_vector"].shape == (40,)
     finally:
         for p in paths:
             os.unlink(p)