diff --git a/test/test_functional.py b/test/test_functional.py
index 43aa885506..1125427c0c 100644
--- a/test/test_functional.py
+++ b/test/test_functional.py
@@ -560,7 +560,7 @@ def test_mask_along_axis(self):
         specgram = torch.randn(2, 1025, 400)
         mask_param = 100
         mask_value = 30.
-        axis = 2
+        axis = -1
 
         _test_torchscript_functional(F.mask_along_axis, specgram, mask_param, mask_value, axis)
 
@@ -569,7 +569,7 @@ def test_mask_along_axis_iid(self):
         specgrams = torch.randn(4, 2, 1025, 400)
         mask_param = 100
         mask_value = 30.
-        axis = 2
+        axis = -2
 
         _test_torchscript_functional(F.mask_along_axis_iid, specgrams, mask_param, mask_value, axis)
 
@@ -651,7 +651,7 @@ def test_complex_norm(complex_tensor, power):
 ])
 @pytest.mark.parametrize('mask_param', [100])
 @pytest.mark.parametrize('mask_value', [0., 30.])
-@pytest.mark.parametrize('axis', [1, 2])
+@pytest.mark.parametrize('axis', [-2, -1])
 def test_mask_along_axis(specgram, mask_param, mask_value, axis):
 
     mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis)
@@ -671,7 +671,7 @@ def test_mask_along_axis(specgram, mask_param, mask_value, axis):
 ])
 @pytest.mark.parametrize('mask_param', [100])
 @pytest.mark.parametrize('mask_value', [0., 30.])
-@pytest.mark.parametrize('axis', [2, 3])
+@pytest.mark.parametrize('axis', [-2, -1])
 def test_mask_along_axis_iid(specgrams, mask_param, mask_value, axis):
 
     mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)
diff --git a/torchaudio/functional.py b/torchaudio/functional.py
index 9538d70ed5..68ff6662a0 100644
--- a/torchaudio/functional.py
+++ b/torchaudio/functional.py
@@ -823,13 +823,13 @@ def mask_along_axis_iid(specgrams, mask_param, mask_value, axis):
         specgrams (Tensor): Real spectrograms (batch, channel, freq, time)
         mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
         mask_value (float): Value to assign to the masked columns
-        axis (int): Axis to apply masking on (2 -> frequency, 3 -> time)
+        axis (int): Axis to apply masking on (-2 -> frequency, -1 -> time)
 
     Returns:
         torch.Tensor: Masked spectrograms of dimensions (batch, channel, freq, time)
     """
 
-    if axis != 2 and axis != 3:
+    if axis != -2 and axis != -1:
         raise ValueError('Only Frequency and Time masking are supported')
 
     value = torch.rand(specgrams.shape[:2]) * mask_param
@@ -859,7 +859,7 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
         specgram (Tensor): Real spectrogram (channel, freq, time)
         mask_param (int): Number of columns to be masked will be uniformly sampled from [0, mask_param]
         mask_value (float): Value to assign to the masked columns
-        axis (int): Axis to apply masking on (1 -> frequency, 2 -> time)
+        axis (int): Axis to apply masking on (-2 -> frequency, -1 -> time)
 
     Returns:
         torch.Tensor: Masked spectrogram of dimensions (channel, freq, time)
@@ -876,9 +876,9 @@ def mask_along_axis(specgram, mask_param, mask_value, axis):
     mask_end = (min_value.long() + value.long()).squeeze()
 
     assert mask_end - mask_start < mask_param
-    if axis == 1:
+    if axis == -2:
         specgram[:, mask_start:mask_end] = mask_value
-    elif axis == 2:
+    elif axis == -1:
         specgram[:, :, mask_start:mask_end] = mask_value
     else:
         raise ValueError('Only Frequency and Time masking are supported')
diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py
index 92b336cf92..57453dcce6 100644
--- a/torchaudio/transforms.py
+++ b/torchaudio/transforms.py
@@ -556,7 +556,7 @@ def forward(self, specgram, mask_value=0.):
 
         # if iid_masks flag marked and specgram has a batch dimension
         if self.iid_masks and specgram.dim() == 4:
-            return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis + 1)
+            return F.mask_along_axis_iid(specgram, self.mask_param, mask_value, self.axis)
         else:
             return F.mask_along_axis(specgram, self.mask_param, mask_value, self.axis)
 
@@ -572,7 +572,7 @@ class FrequencyMasking(_AxisMasking):
     """
 
     def __init__(self, freq_mask_param, iid_masks=False):
-        super(FrequencyMasking, self).__init__(freq_mask_param, 1, iid_masks)
+        super(FrequencyMasking, self).__init__(freq_mask_param, -2, iid_masks)
 
 
 class TimeMasking(_AxisMasking):
@@ -586,4 +586,4 @@ class TimeMasking(_AxisMasking):
     """
 
     def __init__(self, time_mask_param, iid_masks=False):
-        super(TimeMasking, self).__init__(time_mask_param, 2, iid_masks)
+        super(TimeMasking, self).__init__(time_mask_param, -1, iid_masks)