# permutationInvariantSISNR

## Syntax

## Description

returns the scale-invariant signal-to-noise ratio (SI-SNR) using the ordering of reference
signals that yields the optimal value for the given processed signals. This metric is
invariant to the permutation of the reference signals, and you can therefore use it to
evaluate a signal separation system without needing the order of the ground truth signals to
align with the system output.`metric`

= permutationInvariantSISNR(`proc`

,`ref`

)

specifies options using one or more name-value arguments. For example,
`metric`

= permutationInvariantSISNR(`proc`

,`ref`

,`Name=Value`

)`permutationInvariantSISNR(proc,ref,SubtractMean=false)`

does not
subtract the means from individual signals before computing the permutation invariant
SI-SNR.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

## References

[1] Kolbaek, Morten, Dong Yu,
Zheng-Hua Tan, and Jesper Jensen. “Multitalker Speech Separation With Utterance-Level
Permutation Invariant Training of Deep Recurrent Neural Networks.” *IEEE/ACM
Transactions on Audio, Speech, and Language Processing* 25, no. 10 (October
2017): 1901–13. https://doi.org/10.1109/TASLP.2017.2726762.

[2] Takahashi, Naoya, Sudarsanam
Parthasaarathy, Nabarun Goswami, and Yuki Mitsufuji. “Recursive Speech Separation for Unknown
Number of Speakers.” In *Interspeech 2019*, 1348–52. ISCA, 2019.
https://doi.org/10.21437/Interspeech.2019-1550.

[3] Yu, Dong, Morten Kolbaek,
Zheng-Hua Tan, and Jesper Jensen. “Permutation Invariant Training of Deep Models for
Speaker-Independent Multi-Talker Speech Separation.” In *2017 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 241–45. New
Orleans, LA: IEEE, 2017. https://doi.org/10.1109/ICASSP.2017.7952154.

## Extended Capabilities

## Version History

**Introduced in R2024b**