diff --git a/audformat/core/database.py b/audformat/core/database.py
index 4162785d..ca6a261b 100644
--- a/audformat/core/database.py
+++ b/audformat/core/database.py
@@ -27,6 +27,8 @@
 from audformat.core.errors import BadKeyError
 from audformat.core.errors import TableExistsError
 from audformat.core.index import filewise_index
+from audformat.core.index import is_filewise_index
+from audformat.core.index import is_segmented_index
 from audformat.core.media import Media
 from audformat.core.rater import Rater
 from audformat.core.scheme import Scheme
@@ -867,6 +869,21 @@ def scheme_in_column(scheme_id, column, column_id):
                     original_column_names=original_column_names,
                     aggregate_function=aggregate_function,
                 )
+            # Expand filewise labels to segments
+            if is_segmented_index(obj) and is_filewise_index(additional_obj):
+                print(f"{obj=}")
+                print(f"{obj.index.get_level_values(define.IndexField.FILE)=}")
+                common_files = obj.index.get_level_values(define.IndexField.FILE)
+                # Problem of next line:
+                # squeezes [f1, f1, f2] to [f1, f2]
+                # so we need to separate intersection from listing files
+                # common_files = utils.intersect([additional_obj.index, files])
+                print(f"{files=}")
+                additional_obj = additional_obj.loc[files]
+                print(f"{additional_obj=}")
+                print(f"{obj.loc[files]=}")
+                print(f"{obj.loc[files].index=}")
+                additional_obj.index = obj.loc[files].index
             objs.append(additional_obj)
         if len(objs) > 1:
             obj = utils.concat(objs)
diff --git a/tests/test_database_get.py b/tests/test_database_get.py
index 83ebd312..ec17c6eb 100644
--- a/tests/test_database_get.py
+++ b/tests/test_database_get.py
@@ -23,6 +23,7 @@ def mono_db(tmpdir):
     db.schemes["age"] = audformat.Scheme("int", minimum=0)
     db.schemes["height"] = audformat.Scheme("float")
     db.schemes["int"] = audformat.Scheme("int")
+    db.schemes["partial"] = audformat.Scheme("str")
     db.schemes["rating"] = audformat.Scheme("int", labels=[0, 1, 2])
     db.schemes["regression"] = audformat.Scheme("float")
     db.schemes["selection"] = audformat.Scheme("int", labels=[0, 1])
@@ -103,6 +104,8 @@ def mono_db(tmpdir):
     db["files.sub"]["text"].set("a")
     db["files.sub"]["numbers"] = audformat.Column(scheme_id="int")
     db["files.sub"]["numbers"].set(0)
+    db["files.sub"]["partial"] = audformat.Column(scheme_id="partial")
+    db["files.sub"]["partial"].set("a")
 
     index = audformat.filewise_index(["f1.wav", "f3.wav"])
     db["other"] = audformat.Table(index)
@@ -542,6 +545,69 @@ def wrong_scheme_labels_db(tmpdir):
                 dtype="float",
             ),
         ),
+        (
+            "mono_db",
+            "regression",
+            ["speaker"],
+            pd.concat(
+                [
+                    pd.Series(
+                        [0.3, 0.2, 0.6, 0.4],
+                        index=audformat.segmented_index(
+                            ["f1.wav", "f1.wav", "f1.wav", "f2.wav"],
+                            [0, 0.1, 0.3, 0],
+                            [0.2, 0.2, 0.5, 0.7],
+                        ),
+                        dtype="float",
+                        name="regression",
+                    ),
+                    pd.Series(
+                        ["s1", "s1", "s1", "s2"],
+                        index=audformat.segmented_index(
+                            ["f1.wav", "f1.wav", "f1.wav", "f2.wav"],
+                            [0, 0.1, 0.3, 0],
+                            [0.2, 0.2, 0.5, 0.7],
+                        ),
+                        dtype=pd.CategoricalDtype(
+                            ["s1", "s2", "s3"],
+                            ordered=False,
+                        ),
+                        name="speaker",
+                    ),
+                ],
+                axis=1,
+            ),
+        ),
+        (
+            "mono_db",
+            "regression",
+            ["partial"],
+            pd.concat(
+                [
+                    pd.Series(
+                        [0.3, 0.2, 0.6, 0.4],
+                        index=audformat.segmented_index(
+                            ["f1.wav", "f1.wav", "f1.wav", "f2.wav"],
+                            [0, 0.1, 0.3, 0],
+                            [0.2, 0.2, 0.5, 0.7],
+                        ),
+                        dtype="float",
+                        name="regression",
+                    ),
+                    pd.Series(
+                        ["a", "a", "a", None],
+                        index=audformat.segmented_index(
+                            ["f1.wav", "f1.wav", "f1.wav", "f2.wav"],
+                            [0, 0.1, 0.3, 0],
+                            [0.2, 0.2, 0.5, 0.7],
+                        ),
+                        dtype="string",
+                        name="partial",
+                    ),
+                ],
+                axis=1,
+            ),
+        ),
         (
             "mono_db",
             "selection",