Scale-Invariant Signal-to-Noise Ratio (SI-SNR)¶
Module Interface¶
- class torchmetrics.ScaleInvariantSignalNoiseRatio(**kwargs)[source]
Scale-invariant signal-to-noise ratio (SI-SNR).
Forward accepts
preds
:shape [...,time]
target
:shape [...,time]
- Parameters
kwargs¶ (
Any
) – Additional keyword arguments, see Advanced metric settings for more info.- 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.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Functional Interface¶
- torchmetrics.functional.scale_invariant_signal_noise_ratio(preds, target)[source]
Scale-invariant signal-to-noise ratio (SI-SNR).
- Parameters
- Return type
- 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.