diff --git a/asteroid/utils/test_utils.py b/asteroid/utils/test_utils.py
new file mode 100644
index 000000000..6de8bc77d
--- /dev/null
+++ b/asteroid/utils/test_utils.py
@@ -0,0 +1,14 @@
+import torch
+from torch.utils import data
+
+
+class DummyDataset(data.Dataset):
+    def __init__(self):
+        self.inp_dim = 10
+        self.out_dim = 10
+
+    def __len__(self):
+        return 20
+
+    def __getitem__(self, idx):
+        return torch.randn(1, self.inp_dim), torch.randn(1, self.out_dim)
diff --git a/tests/engine/scheduler_test.py b/tests/engine/scheduler_test.py
new file mode 100644
index 000000000..88fee7814
--- /dev/null
+++ b/tests/engine/scheduler_test.py
@@ -0,0 +1,64 @@
+from torch import nn, optim
+from torch.utils import data
+from pytorch_lightning import Trainer
+
+
+from asteroid.engine.system import System
+from asteroid.utils.test_utils import DummyDataset
+from asteroid.engine.schedulers import NoamScheduler, DPTNetScheduler
+
+
+def common_setup():
+    model = nn.Sequential(nn.Linear(10, 10), nn.ReLU())
+    optimizer = optim.Adam(model.parameters(), lr=1e-3)
+    dataset = DummyDataset()
+    loader = data.DataLoader(dataset, batch_size=2, num_workers=4)
+    trainer = Trainer(max_epochs=1, fast_dev_run=True)
+    return model, optimizer, loader, trainer
+
+
+def test_state_dict():
+    """ Load and serialize scheduler. """
+    model, optimizer, loader, trainer = common_setup()
+    sched = NoamScheduler(optimizer, d_model=10, warmup_steps=100)
+    state_dict = sched.state_dict()
+    sched.load_state_dict(state_dict)
+    state_dict_c = sched.state_dict()
+    assert state_dict == state_dict_c
+
+
+def test_noam_scheduler():
+    model, optimizer, loader, trainer = common_setup()
+    scheduler = {
+        "scheduler": NoamScheduler(optimizer, d_model=10, warmup_steps=100),
+        "interval": "batch",
+    }
+
+    system = System(
+        model,
+        optimizer,
+        loss_func=nn.MSELoss(),
+        train_loader=loader,
+        val_loader=loader,
+        scheduler=scheduler,
+    )
+    trainer.fit(system)
+
+
+def test_dptnet_scheduler():
+    model, optimizer, loader, trainer = common_setup()
+
+    scheduler = {
+        "scheduler": DPTNetScheduler(optimizer, d_model=10, steps_per_epoch=6, warmup_steps=4),
+        "interval": "batch",
+    }
+
+    system = System(
+        model,
+        optimizer,
+        loss_func=nn.MSELoss(),
+        train_loader=loader,
+        val_loader=loader,
+        scheduler=scheduler,
+    )
+    trainer.fit(system)
diff --git a/tests/engine/system_test.py b/tests/engine/system_test.py
index 073377402..02768df3c 100644
--- a/tests/engine/system_test.py
+++ b/tests/engine/system_test.py
@@ -1,21 +1,9 @@
-import torch
 from torch import nn, optim
 from torch.utils import data
 from pytorch_lightning import Trainer
 
 from asteroid.engine.system import System
-
-
-class DummyDataset(data.Dataset):
-    def __init__(self):
-        self.inp_dim = 10
-        self.out_dim = 10
-
-    def __len__(self):
-        return 20
-
-    def __getitem__(self, idx):
-        return torch.randn(1, self.inp_dim), torch.randn(1, self.out_dim)
+from asteroid.utils.test_utils import DummyDataset
 
 
 def test_system():