diff --git a/CHANGELOG.md b/CHANGELOG.md
index e481173d7a1..aed5276f194 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,7 +28,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added `MinMaxMetric` to wrappers ([#556](https://github.com/PyTorchLightning/metrics/pull/556))
 
 
-- Added `ignore_index` to to retrieval metrics ([#676](https://github.com/PyTorchLightning/metrics/pull/676))
+- Added `ignore_index` to retrieval metrics ([#676](https://github.com/PyTorchLightning/metrics/pull/676))
 
 
 - Added support for multi references in `ROUGEScore` ([#680](https://github.com/PyTorchLightning/metrics/pull/680))
@@ -71,6 +71,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `SI_SDR` -> `ScaleInvariantSignalDistortionRatio`
 
 
+- Renamed audio SNR metrics: ([#712](https://github.com/PyTorchLightning/metrics/pull/712))
+  * `functional.snr` -> `functional.signal_distortion_ratio`
+  * `functional.si_snr` -> `functional.scale_invariant_signal_noise_ratio`
+  * `SNR` -> `SignalNoiseRatio`
+  * `SI_SNR` -> `ScaleInvariantSignalNoiseRatio`
+
+
 - Renamed image metrics ([#732](https://github.com/PyTorchLightning/metrics/pull/732))
   * `functional.psnr` -> `functional.peak_signal_noise_ratio`
   * `PSNR` -> `PeakSignalNoiseRatio`
diff --git a/README.md b/README.md
index 83a11254556..bb0eb81bce4 100644
--- a/README.md
+++ b/README.md
@@ -267,8 +267,8 @@ We currently have implemented metrics within the following domains:
 
 - Audio (
   [ScaleInvariantSignalDistortionRatio](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#ScaleInvariantSignalDistortionRatio),
-  [SI_SNR](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#si-snr),
-  [SNR](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#snr)
+  [ScaleInvariantSignalNoiseRatio](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#ScaleInvariantSignalNoiseRatio),
+  [SignalNoiseRatio](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#SignalNoiseRatio)
   and [few more](https://torchmetrics.readthedocs.io/en/latest/references/modules.html#audio-metrics)
   )
 - Classification (
diff --git a/docs/source/references/functional.rst b/docs/source/references/functional.rst
index 96aae85d36c..766850d98bd 100644
--- a/docs/source/references/functional.rst
+++ b/docs/source/references/functional.rst
@@ -38,17 +38,17 @@ scale_invariant_signal_distortion_ratio [func]
     :noindex:
 
 
-si_snr [func]
-~~~~~~~~~~~~~
+scale_invariant_signal_noise_ratio [func]
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: torchmetrics.functional.si_snr
     :noindex:
 
 
-snr [func]
-~~~~~~~~~~
+signal_noise_ratio [func]
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: torchmetrics.functional.snr
+.. autofunction:: torchmetrics.functional.signal_noise_ratio
     :noindex:
 
 
diff --git a/docs/source/references/modules.rst b/docs/source/references/modules.rst
index 74dcdb4f46e..c684925c074 100644
--- a/docs/source/references/modules.rst
+++ b/docs/source/references/modules.rst
@@ -66,12 +66,17 @@ the metric will be computed over the ``time`` dimension.
 .. doctest::
 
     >>> import torch
+    >>> from torchmetrics import SignalNoiseRatio
+    >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
+    >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
+    >>> snr = SignalNoiseRatio()
+    >>> snr(preds, target)
+    tensor(16.1805)
     >>> from torchmetrics import SNR
     >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
     >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
-    >>> snr = SNR()
-    >>> snr_val = snr(preds, target)
-    >>> snr_val
+    >>> snr = SignalNoiseRatio()
+    >>> snr(preds, target)
     tensor(16.1805)
 
 PESQ
@@ -97,16 +102,16 @@ ScaleInvariantSignalDistortionRatio
 .. autoclass:: torchmetrics.ScaleInvariantSignalDistortionRatio
     :noindex:
 
-SI_SNR
-~~~~~~
+ScaleInvariantSignalNoiseRatio
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: torchmetrics.SI_SNR
+.. autoclass:: torchmetrics.ScaleInvariantSignalNoiseRatio
     :noindex:
 
-SNR
-~~~
+SignalNoiseRatio
+~~~~~~~~~~~~~~~~
 
-.. autoclass:: torchmetrics.SNR
+.. autoclass:: torchmetrics.SignalNoiseRatio
     :noindex:
 
 STOI
diff --git a/tests/audio/test_si_snr.py b/tests/audio/test_si_snr.py
index 063790b3579..6a3ad256daa 100644
--- a/tests/audio/test_si_snr.py
+++ b/tests/audio/test_si_snr.py
@@ -21,8 +21,8 @@
 
 from tests.helpers import seed_all
 from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, MetricTester
-from torchmetrics.audio import SI_SNR
-from torchmetrics.functional import si_snr
+from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
+from torchmetrics.functional import scale_invariant_signal_noise_ratio
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6
 
 seed_all(42)
@@ -79,7 +79,7 @@ def test_si_snr(self, preds, target, sk_metric, ddp, dist_sync_on_step):
             ddp,
             preds,
             target,
-            SI_SNR,
+            ScaleInvariantSignalNoiseRatio,
             sk_metric=partial(average_metric, metric_func=sk_metric),
             dist_sync_on_step=dist_sync_on_step,
         )
@@ -88,12 +88,17 @@ def test_si_snr_functional(self, preds, target, sk_metric):
         self.run_functional_metric_test(
             preds,
             target,
-            si_snr,
+            scale_invariant_signal_noise_ratio,
             sk_metric,
         )
 
     def test_si_snr_differentiability(self, preds, target, sk_metric):
-        self.run_differentiability_test(preds=preds, target=target, metric_module=SI_SNR, metric_functional=si_snr)
+        self.run_differentiability_test(
+            preds=preds,
+            target=target,
+            metric_module=ScaleInvariantSignalNoiseRatio,
+            metric_functional=scale_invariant_signal_noise_ratio,
+        )
 
     @pytest.mark.skipif(
         not _TORCH_GREATER_EQUAL_1_6, reason="half support of core operations on not support before pytorch v1.6"
@@ -103,10 +108,15 @@ def test_si_snr_half_cpu(self, preds, target, sk_metric):
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
     def test_si_snr_half_gpu(self, preds, target, sk_metric):
-        self.run_precision_test_gpu(preds=preds, target=target, metric_module=SI_SNR, metric_functional=si_snr)
+        self.run_precision_test_gpu(
+            preds=preds,
+            target=target,
+            metric_module=ScaleInvariantSignalNoiseRatio,
+            metric_functional=scale_invariant_signal_noise_ratio,
+        )
 
 
-def test_error_on_different_shape(metric_class=SI_SNR):
+def test_error_on_different_shape(metric_class=ScaleInvariantSignalNoiseRatio):
     metric = metric_class()
     with pytest.raises(RuntimeError, match="Predictions and targets are expected to have the same shape"):
         metric(torch.randn(100), torch.randn(50))
diff --git a/tests/audio/test_snr.py b/tests/audio/test_snr.py
index ad63f99c9b5..1ab29a63049 100644
--- a/tests/audio/test_snr.py
+++ b/tests/audio/test_snr.py
@@ -22,19 +22,19 @@
 
 from tests.helpers import seed_all
 from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, MetricTester
-from torchmetrics.audio import SNR
-from torchmetrics.functional import snr
+from torchmetrics.audio import SignalNoiseRatio
+from torchmetrics.functional import signal_noise_ratio
 from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_6
 
 seed_all(42)
 
-Time = 100
+TIME = 100
 
 Input = namedtuple("Input", ["preds", "target"])
 
 inputs = Input(
-    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, Time),
-    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, Time),
+    preds=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, TIME),
+    target=torch.rand(NUM_BATCHES, BATCH_SIZE, 1, TIME),
 )
 
 
@@ -86,7 +86,7 @@ def test_snr(self, preds, target, sk_metric, zero_mean, ddp, dist_sync_on_step):
             ddp,
             preds,
             target,
-            SNR,
+            SignalNoiseRatio,
             sk_metric=partial(average_metric, metric_func=sk_metric),
             dist_sync_on_step=dist_sync_on_step,
             metric_args=dict(zero_mean=zero_mean),
@@ -96,14 +96,18 @@ def test_snr_functional(self, preds, target, sk_metric, zero_mean):
         self.run_functional_metric_test(
             preds,
             target,
-            snr,
+            signal_noise_ratio,
             sk_metric,
             metric_args=dict(zero_mean=zero_mean),
         )
 
     def test_snr_differentiability(self, preds, target, sk_metric, zero_mean):
         self.run_differentiability_test(
-            preds=preds, target=target, metric_module=SNR, metric_functional=snr, metric_args={"zero_mean": zero_mean}
+            preds=preds,
+            target=target,
+            metric_module=SignalNoiseRatio,
+            metric_functional=signal_noise_ratio,
+            metric_args={"zero_mean": zero_mean},
         )
 
     @pytest.mark.skipif(
@@ -115,11 +119,15 @@ def test_snr_half_cpu(self, preds, target, sk_metric, zero_mean):
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
     def test_snr_half_gpu(self, preds, target, sk_metric, zero_mean):
         self.run_precision_test_gpu(
-            preds=preds, target=target, metric_module=SNR, metric_functional=snr, metric_args={"zero_mean": zero_mean}
+            preds=preds,
+            target=target,
+            metric_module=SignalNoiseRatio,
+            metric_functional=signal_noise_ratio,
+            metric_args={"zero_mean": zero_mean},
         )
 
 
-def test_error_on_different_shape(metric_class=SNR):
+def test_error_on_different_shape(metric_class=SignalNoiseRatio):
     metric = metric_class()
     with pytest.raises(RuntimeError, match="Predictions and targets are expected to have the same shape"):
         metric(torch.randn(100), torch.randn(50))
diff --git a/torchmetrics/__init__.py b/torchmetrics/__init__.py
index d53d3481a19..93d178aecd9 100644
--- a/torchmetrics/__init__.py
+++ b/torchmetrics/__init__.py
@@ -20,7 +20,9 @@
     SI_SNR,
     SNR,
     ScaleInvariantSignalDistortionRatio,
+    ScaleInvariantSignalNoiseRatio,
     SignalDistortionRatio,
+    SignalNoiseRatio,
 )
 from torchmetrics.classification import (  # noqa: E402, F401
     AUC,
@@ -154,6 +156,8 @@
     "ScaleInvariantSignalDistortionRatio",
     "SI_SDR",
     "SI_SNR",
+    "ScaleInvariantSignalNoiseRatio",
+    "SignalNoiseRatio",
     "SNR",
     "SpearmanCorrcoef",
     "SpearmanCorrCoef",
diff --git a/torchmetrics/audio/__init__.py b/torchmetrics/audio/__init__.py
index 092cb1bd60b..8dfefb1abc0 100644
--- a/torchmetrics/audio/__init__.py
+++ b/torchmetrics/audio/__init__.py
@@ -15,4 +15,4 @@
 from torchmetrics.audio.sdr import SDR, ScaleInvariantSignalDistortionRatio, SignalDistortionRatio  # noqa: F401
 from torchmetrics.audio.si_sdr import SI_SDR  # noqa: F401
 from torchmetrics.audio.si_snr import SI_SNR  # noqa: F401
-from torchmetrics.audio.snr import SNR  # noqa: F401
+from torchmetrics.audio.snr import SNR, ScaleInvariantSignalNoiseRatio, SignalNoiseRatio  # noqa: F401
diff --git a/torchmetrics/audio/si_snr.py b/torchmetrics/audio/si_snr.py
index 82a30bbabe2..a88ad21fc8c 100644
--- a/torchmetrics/audio/si_snr.py
+++ b/torchmetrics/audio/si_snr.py
@@ -12,61 +12,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import Any, Callable, Optional
+from warnings import warn
 
-from torch import Tensor, tensor
+from torch import Tensor
 
-from torchmetrics.functional.audio.si_snr import si_snr
-from torchmetrics.metric import Metric
+from torchmetrics.audio.snr import ScaleInvariantSignalNoiseRatio
 
 
-class SI_SNR(Metric):
+class SI_SNR(ScaleInvariantSignalNoiseRatio):
     """Scale-invariant signal-to-noise ratio (SI-SNR).
 
-    Forward accepts
-
-    - ``preds``: ``shape [...,time]``
-    - ``target``: ``shape [...,time]``
-
-    Args:
-        compute_on_step:
-            Forward only calls ``update()`` and returns None if this is set to False.
-        dist_sync_on_step:
-            Synchronize metric state across processes at each ``forward()``
-            before returning the value at the step.
-        process_group:
-            Specify the process group on which synchronization is called.
-        dist_sync_fn:
-            Callback that performs the allgather operation on the metric state. When `None`, DDP
-            will be used to perform the allgather.
-
-    Raises:
-        TypeError:
-            if target and preds have a different shape
-
-    Returns:
-        average si-snr value
+    .. deprecated:: v0.7
+        Use :class:`torchmetrics.ScaleInvariantSignalNoiseRatio`. Will be removed in v0.8.
 
     Example:
         >>> import torch
-        >>> from torchmetrics import SI_SNR
-        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
-        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
         >>> si_snr = SI_SNR()
-        >>> si_snr_val = si_snr(preds, target)
-        >>> si_snr_val
+        >>> si_snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
         tensor(15.0918)
-
-    References:
-        [1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
-        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
-        696-700, doi: 10.1109/ICASSP.2018.8462116.
     """
 
-    is_differentiable = True
-    sum_si_snr: Tensor
-    total: Tensor
-    higher_is_better = True
-
     def __init__(
         self,
         compute_on_step: bool = True,
@@ -74,28 +39,8 @@ def __init__(
         process_group: Optional[Any] = None,
         dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
     ) -> None:
-        super().__init__(
-            compute_on_step=compute_on_step,
-            dist_sync_on_step=dist_sync_on_step,
-            process_group=process_group,
-            dist_sync_fn=dist_sync_fn,
+        warn(
+            "`SI_SNR` was renamed to `ScaleInvariantSignalNoiseRatio` in v0.7 and it will be removed in v0.8",
+            DeprecationWarning,
         )
-
-        self.add_state("sum_si_snr", default=tensor(0.0), dist_reduce_fx="sum")
-        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
-
-    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
-        """Update state with predictions and targets.
-
-        Args:
-            preds: Predictions from model
-            target: Ground truth values
-        """
-        si_snr_batch = si_snr(preds=preds, target=target)
-
-        self.sum_si_snr += si_snr_batch.sum()
-        self.total += si_snr_batch.numel()
-
-    def compute(self) -> Tensor:
-        """Computes average SI-SNR."""
-        return self.sum_si_snr / self.total
+        super().__init__(compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
diff --git a/torchmetrics/audio/snr.py b/torchmetrics/audio/snr.py
index e42078e7b69..06280ab728a 100644
--- a/torchmetrics/audio/snr.py
+++ b/torchmetrics/audio/snr.py
@@ -12,14 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import Any, Callable, Optional
+from warnings import warn
 
 from torch import Tensor, tensor
 
-from torchmetrics.functional.audio.snr import snr
+from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, snr
 from torchmetrics.metric import Metric
 
 
-class SNR(Metric):
+class SignalNoiseRatio(Metric):
     r"""Signal-to-noise ratio (SNR_):
 
     .. math::
@@ -57,12 +58,11 @@ class SNR(Metric):
 
     Example:
         >>> import torch
-        >>> from torchmetrics import SNR
+        >>> from torchmetrics import SignalNoiseRatio
         >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
         >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
-        >>> snr = SNR()
-        >>> snr_val = snr(preds, target)
-        >>> snr_val
+        >>> snr = SignalNoiseRatio()
+        >>> snr(preds, target)
         tensor(16.1805)
 
     References:
@@ -109,3 +109,110 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
     def compute(self) -> Tensor:
         """Computes average SNR."""
         return self.sum_snr / self.total
+
+
+class SNR(SignalNoiseRatio):
+    r"""Signal-to-noise ratio (SNR_):
+
+    .. deprecated:: v0.7
+        Use :class:`torchmetrics.SignalNoiseRatio`. Will be removed in v0.8.
+
+    Example:
+        >>> import torch
+        >>> snr = SNR()
+        >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
+        tensor(16.1805)
+
+    """
+
+    def __init__(
+        self,
+        zero_mean: bool = False,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
+    ) -> None:
+        warn("`SNR` was renamed to `SignalNoiseRatio` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+        super().__init__(zero_mean, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
+
+
+class ScaleInvariantSignalNoiseRatio(Metric):
+    """Scale-invariant signal-to-noise ratio (SI-SNR).
+
+    Forward accepts
+
+    - ``preds``: ``shape [...,time]``
+    - ``target``: ``shape [...,time]``
+
+    Args:
+        compute_on_step:
+            Forward only calls ``update()`` and returns None if this is set to False.
+        dist_sync_on_step:
+            Synchronize metric state across processes at each ``forward()``
+            before returning the value at the step.
+        process_group:
+            Specify the process group on which synchronization is called.
+        dist_sync_fn:
+            Callback that performs the allgather operation on the metric state. When `None`, DDP
+            will be used to perform the allgather.
+
+    Raises:
+        TypeError:
+            if target and preds have a different shape
+
+    Returns:
+        average si-snr value
+
+    Example:
+        >>> import torch
+        >>> from torchmetrics import ScaleInvariantSignalNoiseRatio
+        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
+        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
+        >>> si_snr = ScaleInvariantSignalNoiseRatio()
+        >>> si_snr(preds, target)
+        tensor(15.0918)
+
+    References:
+        [1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
+        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
+        696-700, doi: 10.1109/ICASSP.2018.8462116.
+    """
+
+    is_differentiable = True
+    sum_si_snr: Tensor
+    total: Tensor
+    higher_is_better = True
+
+    def __init__(
+        self,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+        dist_sync_fn: Optional[Callable[[Tensor], Tensor]] = None,
+    ) -> None:
+        super().__init__(
+            compute_on_step=compute_on_step,
+            dist_sync_on_step=dist_sync_on_step,
+            process_group=process_group,
+            dist_sync_fn=dist_sync_fn,
+        )
+
+        self.add_state("sum_si_snr", default=tensor(0.0), dist_reduce_fx="sum")
+        self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
+
+    def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
+        """Update state with predictions and targets.
+
+        Args:
+            preds: Predictions from model
+            target: Ground truth values
+        """
+        si_snr_batch = scale_invariant_signal_noise_ratio(preds=preds, target=target)
+
+        self.sum_si_snr += si_snr_batch.sum()
+        self.total += si_snr_batch.numel()
+
+    def compute(self) -> Tensor:
+        """Computes average SI-SNR."""
+        return self.sum_si_snr / self.total
diff --git a/torchmetrics/functional/__init__.py b/torchmetrics/functional/__init__.py
index 3ba382bbd9c..006c77732a2 100644
--- a/torchmetrics/functional/__init__.py
+++ b/torchmetrics/functional/__init__.py
@@ -15,7 +15,7 @@
 from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio, sdr, signal_distortion_ratio
 from torchmetrics.functional.audio.si_sdr import si_sdr
 from torchmetrics.functional.audio.si_snr import si_snr
-from torchmetrics.functional.audio.snr import snr
+from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, signal_noise_ratio, snr
 from torchmetrics.functional.classification.accuracy import accuracy
 from torchmetrics.functional.classification.auc import auc
 from torchmetrics.functional.classification.auroc import auroc
@@ -133,7 +133,9 @@
     "si_sdr",
     "scale_invariant_signal_distortion_ratio",
     "si_snr",
+    "scale_invariant_signal_noise_ratio",
     "snr",
+    "signal_noise_ratio",
     "spearman_corrcoef",
     "specificity",
     "squad",
diff --git a/torchmetrics/functional/audio/__init__.py b/torchmetrics/functional/audio/__init__.py
index db7f29ecbd3..3cd5c6b73bd 100644
--- a/torchmetrics/functional/audio/__init__.py
+++ b/torchmetrics/functional/audio/__init__.py
@@ -19,4 +19,4 @@
 )
 from torchmetrics.functional.audio.si_sdr import si_sdr  # noqa: F401
 from torchmetrics.functional.audio.si_snr import si_snr  # noqa: F401
-from torchmetrics.functional.audio.snr import snr  # noqa: F401
+from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio, signal_noise_ratio, snr  # noqa: F401
diff --git a/torchmetrics/functional/audio/si_snr.py b/torchmetrics/functional/audio/si_snr.py
index a967ddc306c..65acdd8f59c 100644
--- a/torchmetrics/functional/audio/si_snr.py
+++ b/torchmetrics/functional/audio/si_snr.py
@@ -11,36 +11,26 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from warnings import warn
+
 from torch import Tensor
 
-from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio
+from torchmetrics.functional.audio.snr import scale_invariant_signal_noise_ratio
 
 
 def si_snr(preds: Tensor, target: Tensor) -> Tensor:
     """Scale-invariant signal-to-noise ratio (SI-SNR).
 
-    Args:
-        preds:
-            shape ``[...,time]``
-        target:
-            shape ``[...,time]``
-
-    Returns:
-        si-snr value of shape [...]
+    .. deprecated:: v0.7
+        Use :func:`torchmetrics.functional.scale_invariant_signal_noise_ratio`. Will be removed in v0.8.
 
     Example:
         >>> import torch
-        >>> from torchmetrics.functional.audio import si_snr
-        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
-        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
-        >>> si_snr_val = si_snr(preds, target)
-        >>> si_snr_val
+        >>> si_snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
         tensor(15.0918)
-
-    References:
-        [1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
-        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
-        696-700, doi: 10.1109/ICASSP.2018.8462116.
     """
-
-    return scale_invariant_signal_distortion_ratio(target=target, preds=preds, zero_mean=True)
+    warn(
+        "`si_snr` was renamed to `scale_invariant_signal_noise_ratio` in v0.7 and it will be removed in v0.8",
+        DeprecationWarning,
+    )
+    return scale_invariant_signal_noise_ratio(preds, target)
diff --git a/torchmetrics/functional/audio/snr.py b/torchmetrics/functional/audio/snr.py
index dc1fc42e9c1..8a199c07cbf 100644
--- a/torchmetrics/functional/audio/snr.py
+++ b/torchmetrics/functional/audio/snr.py
@@ -11,13 +11,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from warnings import warn
+
 import torch
 from torch import Tensor
 
+from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio
 from torchmetrics.utilities.checks import _check_same_shape
 
 
-def snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
+def signal_noise_ratio(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
     r"""Signal-to-noise ratio (SNR_):
 
     .. math::
@@ -39,11 +42,10 @@ def snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
         snr value of shape [...]
 
     Example:
-        >>> from torchmetrics.functional.audio import snr
+        >>> from torchmetrics.functional.audio import signal_noise_ratio
         >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
         >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
-        >>> snr_val = snr(preds, target)
-        >>> snr_val
+        >>> signal_noise_ratio(preds, target)
         tensor(16.1805)
 
     References:
@@ -64,3 +66,46 @@ def snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
     snr_value = 10 * torch.log10(snr_value)
 
     return snr_value
+
+
+def snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
+    r"""Signal-to-noise ratio (SNR_)
+
+    .. deprecated:: v0.7
+        Use :func:`torchmetrics.functional.signal_noise_ratio`. Will be removed in v0.8.
+
+    Example:
+        >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
+        tensor(16.1805)
+
+    """
+    warn("`snr` was renamed to `signal_noise_ratio` in v0.7 and it will be removed in v0.8", DeprecationWarning)
+    return signal_noise_ratio(preds, target, zero_mean)
+
+
+def scale_invariant_signal_noise_ratio(preds: Tensor, target: Tensor) -> Tensor:
+    """Scale-invariant signal-to-noise ratio (SI-SNR).
+
+    Args:
+        preds:
+            shape ``[...,time]``
+        target:
+            shape ``[...,time]``
+
+    Returns:
+        si-snr value of shape [...]
+
+    Example:
+        >>> import torch
+        >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
+        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
+        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
+        >>> scale_invariant_signal_noise_ratio(preds, target)
+        tensor(15.0918)
+
+    References:
+        [1] Y. Luo and N. Mesgarani, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
+        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
+        696-700, doi: 10.1109/ICASSP.2018.8462116.
+    """
+    return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=True)