# sisnr

## Description

## Examples

## Input Arguments

## Output Arguments

## Algorithms

The scale-invariant signal-to-noise ratio (SI-SNR) measures the level of distortion or noise in a processed signal by comparing it to a reference signal in a way that is invariant to the scaling of the signals. This metric is useful for evaluating speech enhancement and source separation systems.

The `sisnr`

function calculates the SI-SNR according to the
following formula, where *s* is the reference signal and *ŝ*
is the processed signal.

$$\text{SI-SNR}=10{\mathrm{log}}_{10}\left(\frac{{\Vert \alpha s\Vert}^{2}}{{\Vert \alpha s-\widehat{s}\Vert}^{2}}\right)\text{,where}\alpha =\frac{{\widehat{s}}^{T}s}{{\Vert s\Vert}^{2}}$$

By default, the `sisnr`

function subtracts the mean to
zero-center the signal before calculating the SI-SNR. You can skip this step by setting
`SubtractMean`

to `false`

, and the resulting metric is
commonly referred to as the scale-invariant signal-to-distortion ratio (SI-SDR).

## References

[1] Roux, Jonathan Le, Scott Wisdom,
Hakan Erdogan, and John R. Hershey. “SDR – Half-Baked or Well Done?” In *ICASSP 2019
- 2019 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP)*, 626–30. Brighton, United Kingdom: IEEE, 2019.
https://doi.org/10.1109/ICASSP.2019.8683855.

## Extended Capabilities

## Version History

**Introduced in R2024b**