7 papers
Monitoring Pasture Restoration from Satellite Image Time Series: Caveats and Opportunities
Linnea Sartorius, Isak Randahl, Delia Fano Yela +3
Monitoring nature restoration at scale is an important but difficult ecological problem. Deep learning methods to analyze satellite image time series (SITS) have been widely used f…
Grazing Detection using Deep Learning and Sentinel-2 Time Series Data
Aleksis Pirinen, Delia Fano Yela, Smita Chakraborty +1
Grazing shapes both agricultural production and biodiversity, yet scalable monitoring of where grazing occurs remains limited. We study seasonal grazing detection from Sentinel-2 L…
Spectral Visibility Graphs: Application to Similarity of Harmonic Signals
Delia Fano Yela, Dan Stowell, Mark Sandler
Graph theory is emerging as a new source of tools for time series analysis. One promising method is to transform a signal into its visibility graph, a representation which captures…
Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation
Delia Fano Yela, Dan Stowell, Mark Sandler
Kernel Additive Modelling (KAM) is a framework for source separation aiming to explicitly model inherent properties of sound sources to help with their identification and separatio…
Shift-Invariant Kernel Additive Modelling for Audio Source Separation
Delia Fano Yela, Sebastian Ewert, Ken O'Hanlon +1
A major goal in blind source separation to identify and separate sources is to model their inherent characteristics. While most state-of-the-art approaches are supervised methods t…
On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study
Delia Fano Yela, Sebastian Ewert, Derry FitzGerald +1
Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying…