'''Statistical signal processing''' is an approach which treats signals as [[stochastic process]]es, utilizing their [[statistical]] properties to perform signal processing tasks.<ref name ="Scharf">{{cite book |first=Louis L. |last=Scharf |title=Statistical signal processing: detection, estimation, and time series analysis |publisher=[[Addison–Wesley]] |___location=[[Boston]] |year=1991 |isbn=0-201-19038-9 |oclc=61160161}}</ref>. Statistical techniques are widely used in signal processing applications. For example, one can model the [[probability distribution]] of noise incurred when photographing an image, and construct techniques based on this model to [[noise reduction|reduce the noise]] in the resulting image.
===Graph ===
'''Graph signal processing''' generalizes signal processing tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.<ref name ="Ortega">{{cite book |first=A. |last=Ortega |title=Introduction to Graph Signal Processing |publisher=[[Cambridge University Press]] |___location=[[Cambridge]] |year=2022 |isbn=9781108552349}}</ref>. Graph signal processing presents several key points such as sampling signal techniques,<ref name="Tanaka">{{cite journal|title=Generalized Sampling on Graphs with Subspace and Smoothness Prior|journal=IEEE Transactions on Signal Processing|date=2020|url=https://ieeexplore.ieee.org/document/9043719|last1=Tanaka|first1=Y.|last2=Eldar|first2=Y.}}</ref>, recovery techniques <ref name="Fascista">{{cite journal|title=Graph Signal Reconstruction under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification|journal=IEEE Transactions on Signal and Information Processing over Networks|date=2024|url=https://ieeexplore.ieee.org/document/10465260|last1=Fascista|first1=A.|last2=Coluccia|first2=A.|last3=Ravazzi|first3=C.}}</ref> and time-varying techiques.<ref name="Giraldo">{{cite journal|title=Reconstruction of Time-varying Graph Signals via Sobolev Smoothness|journal=IEEE Transactions on Signal and Information Processing Over Networks|date=March 2022|url=https://ieeexplore.ieee.org/document/9730033|last1=Giraldo|first1=J.|last2=Mahmood|first2=A. |last3=Garcia-Garcia|first3=B.|last4=Thanou|first4=D.|last5=Bouwmans|first5=T.}}</ref>. Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite journal|title=Semi-Supervised Background Subtraction of Unseen Videos: Minimization of The Total Variation of Graph Signals|journal= IEEE International Conference on Image Processing, ICIP 2020|date=October 2020|url=https://ieeexplore.ieee.org/document/9190887|last1=Giraldo|first1=J.|last2=Bouwmans|first2=T.}}</ref>
<ref name="Giraldo2">{{cite journal|title=GraphBGS: Background Subtraction via Recovery of Graph Signals|journal=IInternational Conference on Pattern Recognition, ICPR 2020|date=2020|url=https://ieeexplore.ieee.org/document/9412999|last1=Giraldo|first1=J.|last2=Bouwmans|first2=T.}}</ref>
<ref name="Giraldo3">{{cite journal|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021|date=February 2021|url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3|last1=Giraldo|first1=J.|last2=Javed|first2=S.|last3=Sultana|first3=M.|last4=Jung|first4=S.|last5=Bouwmans|first5=T.}}</ref> and sound anomaly detection.<ref name="Bouwmans1">{{cite journal|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing|journal=European Conference on Signal Processing, EUSIPCO 2024|date=2024|url=https://ieeexplore.ieee.org/document/10715291|last1=Mnasri|first1=Z.|last2=Giraldo|first2=H. |last3=Bouwmans|first3=T.}}</ref>.
