diff --git a/docs/source/functional.rst b/docs/source/functional.rst
index 60c548f5a9..0f9dd0a58d 100644
--- a/docs/source/functional.rst
+++ b/docs/source/functional.rst
@@ -66,27 +66,6 @@ resample
 
 .. autofunction:: resample
 
-:hidden:`Complex Utility`
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Utilities for pseudo complex tensor. This is not for the native complex dtype, such as `cfloat64`, but for tensors with real-value type and have extra dimension at the end for real and imaginary parts.
-
-angle
------
-
-.. autofunction:: angle
-
-complex_norm
-------------
-
-.. autofunction:: complex_norm
-
-
-magphase
---------
-
-.. autofunction:: magphase
-
 :hidden:`Filtering`
 ~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index c5fa327cca..abde637132 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -87,13 +87,6 @@ Transforms are common audio transforms. They can be chained together using :clas
 
   .. automethod:: forward
 
-:hidden:`ComplexNorm`
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: ComplexNorm
-
-  .. automethod:: forward
-
 :hidden:`ComputeDeltas`
 ~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/test/torchaudio_unittest/functional/functional_impl.py b/test/torchaudio_unittest/functional/functional_impl.py
index 1b18bd255b..d3dabdc801 100644
--- a/test/torchaudio_unittest/functional/functional_impl.py
+++ b/test/torchaudio_unittest/functional/functional_impl.py
@@ -217,16 +217,6 @@ def test_amplitude_to_DB_top_db_clamp(self, shape):
             f"No values were close to the limit. Did it over-clamp?\n{decibels}"
         )
 
-    @parameterized.expand(
-        list(itertools.product([(1, 2, 1025, 400, 2), (1025, 400, 2)], [1, 2, 0.7]))
-    )
-    def test_complex_norm(self, shape, power):
-        torch.random.manual_seed(42)
-        complex_tensor = torch.randn(*shape, dtype=self.dtype, device=self.device)
-        expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2)
-        norm_tensor = F.complex_norm(complex_tensor, power)
-        self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5)
-
     @parameterized.expand(
         list(itertools.product([(2, 1025, 400), (1, 201, 100)], [100], [0., 30.], [1, 2]))
     )
diff --git a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py
index a6424a0acb..59f2b0b06d 100644
--- a/test/torchaudio_unittest/functional/torchscript_consistency_impl.py
+++ b/test/torchaudio_unittest/functional/torchscript_consistency_impl.py
@@ -196,14 +196,6 @@ def func(tensor):
         tensor = torch.rand((1, 10))
         self._assert_consistency(func, tensor)
 
-    def test_complex_norm(self):
-        def func(tensor):
-            power = 2.
-            return F.complex_norm(tensor, power)
-
-        tensor = torch.randn(1, 2, 1025, 400, 2)
-        self._assert_consistency(func, tensor)
-
     def test_mask_along_axis(self):
         def func(tensor):
             mask_param = 100
diff --git a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py
index 750f0d5d62..1a8eb67d7c 100644
--- a/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py
+++ b/test/torchaudio_unittest/transforms/torchscript_consistency_impl.py
@@ -80,10 +80,6 @@ def test_Resample(self):
         tensor = common_utils.get_whitenoise(sample_rate=sr1)
         self._assert_consistency(T.Resample(float(sr1), float(sr2)), tensor)
 
-    def test_ComplexNorm(self):
-        tensor = torch.rand((1, 2, 201, 2))
-        self._assert_consistency(T.ComplexNorm(), tensor)
-
     def test_MuLawEncoding(self):
         tensor = common_utils.get_whitenoise()
         self._assert_consistency(T.MuLawEncoding(), tensor)
diff --git a/torchaudio/functional/__init__.py b/torchaudio/functional/__init__.py
index 5f81b7edf8..b9ac34d7a8 100644
--- a/torchaudio/functional/__init__.py
+++ b/torchaudio/functional/__init__.py
@@ -1,7 +1,5 @@
 from .functional import (
     amplitude_to_DB,
-    angle,
-    complex_norm,
     compute_deltas,
     compute_kaldi_pitch,
     create_dct,
@@ -10,7 +8,6 @@
     DB_to_amplitude,
     detect_pitch_frequency,
     griffinlim,
-    magphase,
     mask_along_axis,
     mask_along_axis_iid,
     mu_law_encoding,
@@ -50,8 +47,6 @@
 
 __all__ = [
     'amplitude_to_DB',
-    'angle',
-    'complex_norm',
     'compute_deltas',
     'compute_kaldi_pitch',
     'create_dct',
@@ -60,7 +55,6 @@
     'DB_to_amplitude',
     'detect_pitch_frequency',
     'griffinlim',
-    'magphase',
     'mask_along_axis',
     'mask_along_axis_iid',
     'mu_law_encoding',
diff --git a/torchaudio/functional/functional.py b/torchaudio/functional/functional.py
index 7982b0b6ce..e05f6f33df 100644
--- a/torchaudio/functional/functional.py
+++ b/torchaudio/functional/functional.py
@@ -3,7 +3,7 @@
 from collections.abc import Sequence
 import io
 import math
-import warnings
+#import warnings
 from typing import Optional, Tuple
 
 import torch
@@ -26,9 +26,6 @@
     "DB_to_amplitude",
     "mu_law_encoding",
     "mu_law_decoding",
-    "complex_norm",
-    "angle",
-    "magphase",
     "phase_vocoder",
     'mask_along_axis',
     'mask_along_axis_iid',
@@ -580,78 +577,6 @@ def mu_law_decoding(
     return x
 
 
-@_mod_utils.deprecated(
-    "Please convert the input Tensor to complex type with `torch.view_as_complex` then "
-    "use `torch.abs`. "
-    "Please refer to https://github.com/pytorch/audio/issues/1337 "
-    "for more details about torchaudio's plan to migrate to native complex type.",
-    version="0.11",
-)
-def complex_norm(
-        complex_tensor: Tensor,
-        power: float = 1.0
-) -> Tensor:
-    r"""Compute the norm of complex tensor input.
-
-    Args:
-        complex_tensor (Tensor): Tensor shape of `(..., complex=2)`
-        power (float): Power of the norm. (Default: `1.0`).
-
-    Returns:
-        Tensor: Power of the normed input tensor. Shape of `(..., )`
-    """
-
-    # Replace by torch.norm once issue is fixed
-    # https://github.com/pytorch/pytorch/issues/34279
-    return complex_tensor.pow(2.).sum(-1).pow(0.5 * power)
-
-
-@_mod_utils.deprecated(
-    "Please convert the input Tensor to complex type with `torch.view_as_complex` then "
-    "use `torch.angle`. "
-    "Please refer to https://github.com/pytorch/audio/issues/1337 "
-    "for more details about torchaudio's plan to migrate to native complex type.",
-    version="0.11",
-)
-def angle(
-        complex_tensor: Tensor
-) -> Tensor:
-    r"""Compute the angle of complex tensor input.
-
-    Args:
-        complex_tensor (Tensor): Tensor shape of `(..., complex=2)`
-
-    Return:
-        Tensor: Angle of a complex tensor. Shape of `(..., )`
-    """
-    return torch.atan2(complex_tensor[..., 1], complex_tensor[..., 0])
-
-
-@_mod_utils.deprecated(
-    "Please convert the input Tensor to complex type with `torch.view_as_complex` then "
-    "use `torch.abs` and `torch.angle`. "
-    "Please refer to https://github.com/pytorch/audio/issues/1337 "
-    "for more details about torchaudio's plan to migrate to native complex type.",
-    version="0.11",
-)
-def magphase(
-        complex_tensor: Tensor,
-        power: float = 1.0
-) -> Tuple[Tensor, Tensor]:
-    r"""Separate a complex-valued spectrogram with shape `(..., 2)` into its magnitude and phase.
-
-    Args:
-        complex_tensor (Tensor): Tensor shape of `(..., complex=2)`
-        power (float): Power of the norm. (Default: `1.0`)
-
-    Returns:
-        (Tensor, Tensor): The magnitude and phase of the complex tensor
-    """
-    mag = complex_norm(complex_tensor, power)
-    phase = angle(complex_tensor)
-    return mag, phase
-
-
 def phase_vocoder(
         complex_specgrams: Tensor,
         rate: float,
diff --git a/torchaudio/transforms.py b/torchaudio/transforms.py
index ac4a3192ad..40411e9550 100644
--- a/torchaudio/transforms.py
+++ b/torchaudio/transforms.py
@@ -25,7 +25,6 @@
     'MuLawEncoding',
     'MuLawDecoding',
     'Resample',
-    'ComplexNorm',
     'TimeStretch',
     'Fade',
     'FrequencyMasking',
@@ -69,6 +68,13 @@ class Spectrogram(torch.nn.Module):
             This argument is only effective when ``power=None``. It is ignored for
             cases where ``power`` is a number as in those cases, the returned tensor is
             power spectrogram, which is a real-valued tensor.
+
+            Example
+
+                    >>> specgram = torch.randn(1, 40, 1000)
+                    >>> specgram = torchaudio.transforms.Spectrogram()(specgram)
+                    >>> specgram.shape
+                        torch.Size([1, 40, 201, 6])
     """
     __constants__ = ['n_fft', 'win_length', 'hop_length', 'pad', 'power', 'normalized']
 
@@ -775,37 +781,6 @@ def forward(self, waveform: Tensor) -> Tensor:
             self.kernel, self.width)
 
 
-class ComplexNorm(torch.nn.Module):
-    r"""Compute the norm of complex tensor input.
-
-    Args:
-        power (float, optional): Power of the norm. (Default: to ``1.0``)
-    """
-    __constants__ = ['power']
-
-    def __init__(self, power: float = 1.0) -> None:
-        warnings.warn(
-            'torchaudio.transforms.ComplexNorm has been deprecated '
-            'and will be removed from future release.'
-            'Please convert the input Tensor to complex type with `torch.view_as_complex` then '
-            'use `torch.abs` and `torch.angle`. '
-            'Please refer to https://github.com/pytorch/audio/issues/1337 '
-            "for more details about torchaudio's plan to migrate to native complex type."
-        )
-        super(ComplexNorm, self).__init__()
-        self.power = power
-
-    def forward(self, complex_tensor: Tensor) -> Tensor:
-        r"""
-        Args:
-            complex_tensor (Tensor): Tensor shape of `(..., complex=2)`.
-
-        Returns:
-            Tensor: norm of the input tensor, shape of `(..., )`.
-        """
-        return F.complex_norm(complex_tensor, self.power)
-
-
 class ComputeDeltas(torch.nn.Module):
     r"""Compute delta coefficients of a tensor, usually a spectrogram.