Signal-to-Noise Ratio (SNR)

Calculate Signal-to-noise ratio (SNR) meric for evaluating quality of audio.

where $P$ denotes the power of each signal. The SNR metric compares the level of the desired signal to the level of background noise. Therefore, a high value of SNR means that the audio is clear.

As input to forward and update the metric accepts the following input

• preds (Tensor): float tensor with shape (...,time)

• target (Tensor): float tensor with shape (...,time)

As output of forward and compute the metric returns the following output

• snr (Tensor): float scalar tensor with average SNR value over samples

Parameters:

• zero_mean (bool) – if to zero mean target and preds or not

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings for more info.

Raises:

TypeError – if target and preds have a different shape

Example

>>> from torch import tensor
>>> from torchmetrics.audio import SignalNoiseRatio
>>> target = tensor([3.0, -0.5, 2.0, 7.0])
>>> preds = tensor([2.5, 0.0, 2.0, 8.0])
>>> snr = SignalNoiseRatio()
>>> snr(preds, target)
tensor(16.1805)

plot(val=None, ax=None)[source]

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>>

>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.audio import SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> metric.update(torch.rand(4), torch.rand(4))
>>> fig_, ax_ = metric.plot()

>>>

>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.audio import SignalNoiseRatio
>>> metric = SignalNoiseRatio()
>>> values = [ ]
>>> for _ in range(10):
...     values.append(metric(torch.rand(4), torch.rand(4)))
>>> fig_, ax_ = metric.plot(values)