diff --git a/services/analysis-engine/tests/test_chord_recognizer.py b/services/analysis-engine/tests/test_chord_recognizer.py
index 20a6dcf7..6a1e99a3 100644
--- a/services/analysis-engine/tests/test_chord_recognizer.py
+++ b/services/analysis-engine/tests/test_chord_recognizer.py
@@ -1,5 +1,6 @@
 """Tests for the chord recognizer module."""
 
+import typing
 from unittest.mock import patch
 
 import numpy as np
@@ -86,7 +87,7 @@ def test_chord_recognizer_rms_padding() -> None:
     y = np.random.randn(SAMPLE_RATE * DURATION_SECONDS)
 
     # Mock RMS to return something shorter than chromagram
-    def mock_rms(*args, **kwargs):
+    def mock_rms(*args: typing.Any, **kwargs: typing.Any) -> np.ndarray:
         return np.array([[0.1, 0.1]])
 
     with patch("librosa.feature.rms", side_effect=mock_rms):
@@ -111,7 +112,7 @@ def test_chord_recognizer_rms_longer() -> None:
     y = np.random.randn(SAMPLE_RATE * DURATION_SECONDS)
 
     # Mock RMS to return something longer than chromagram
-    def mock_rms(*args, **kwargs):
+    def mock_rms(*args: typing.Any, **kwargs: typing.Any) -> np.ndarray:
         # Return a very long array
         return np.array([np.ones(1000)])
 
@@ -300,7 +301,7 @@ def test_chord_recognizer_compute_confidence_downgrade_path() -> None:
 
     original_compute = recognizer._compute_confidence
 
-    def mock_confidence(similarity, best_state):
+    def mock_confidence(similarity: np.ndarray, best_state: int) -> str:
         try:
             return next(confidence_values)
         except StopIteration:
@@ -318,3 +319,57 @@ def mock_confidence(similarity, best_state):
             # Since first frame was high but subsequent were low,
             # the segment confidence should be low (conservative)
             assert non_n[0]["confidence"] == "low"
+
+
+def test_recognize_orchestration() -> None:
+    """Test that recognize properly orchestrates the helper methods."""
+    from unittest.mock import patch
+
+    recognizer = ChordRecognizer()
+    y = np.array([1.0, 2.0, 3.0])
+    sr = 22050
+
+    expected_chords = [{"start_time": 0.0, "end_time": 1.0, "chord": "C", "confidence": "high"}]
+
+    with (
+        patch.object(recognizer, "_separate_harmonic", return_value=y) as mock_separate,
+        patch.object(
+            recognizer, "_extract_chromagram", return_value=np.ones((12, 10))
+        ) as mock_extract,
+        patch.object(recognizer, "_calculate_rms", return_value=np.ones(10)) as mock_rms,
+        patch.object(
+            recognizer,
+            "_match_templates",
+            return_value=(np.ones((24, 10)), np.zeros(10, dtype=int)),
+        ) as mock_match,
+        patch.object(
+            recognizer, "_create_chord_segments", return_value=expected_chords
+        ) as mock_create,
+    ):
+        result = recognizer.recognize(y, sr=sr)
+
+        mock_separate.assert_called_once_with(y)
+        mock_extract.assert_called_once_with(y, sr)
+        mock_rms.assert_called_once()
+        mock_match.assert_called_once()
+        mock_create.assert_called_once()
+
+        assert result == expected_chords
+
+
+def test_create_chord_segments_empty() -> None:
+    """Test _create_chord_segments when no chord segments are created."""
+    from unittest.mock import patch
+
+    recognizer = ChordRecognizer()
+
+    chromagram = np.zeros((12, 0))
+    similarity = np.zeros((24, 0))
+    rms = np.zeros(0)
+
+    with (
+        patch.object(recognizer, "_build_observation_probs", return_value=np.zeros((25, 0))),
+        patch.object(recognizer, "_viterbi_decode", return_value=np.array([], dtype=np.intp)),
+    ):
+        result = recognizer._create_chord_segments(chromagram, similarity, rms, sr=22050)
+        assert result == []