Signal-to-Noise Ratio (SNR)¶
Module Interface¶
- class torchmetrics.SignalNoiseRatio(zero_mean=False, **kwargs)[source]
Calculates Signal-to-noise ratio (SNR) meric for evaluating quality of audio. It is defined as:
where
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
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.
- Raises
TypeError – if target and preds have a different shape
Example
>>> 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)
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.signal_noise_ratio(preds, target, zero_mean=False)[source]
Calculates Signal-to-noise ratio (SNR) meric for evaluating quality of audio. It is defined as:
where
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.
- Parameters
- Return type
- Returns
Float tensor with shape
(...,)
of SNR values per sample- Raises
RuntimeError – If
preds
andtarget
does not have the same shape
Example
>>> 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]) >>> signal_noise_ratio(preds, target) tensor(16.1805)