Sound event localization and detection (SELD) is an important task in machine listening.
Major advancements rely on simulated data with sound events in specific rooms and strong spatio-temporal labels.
SELD data is simulated by convolving spatialy-localized room impulse responses (RIRs) with sound waveforms to place sound events in a soundscape.
However, RIRs require manual collection in specific rooms.
We present SpatialScaper, a library for SELD data simulation and augmentation.
Compared to existing tools, SpatialScaper emulates virtual rooms via parameters such as size and wall absorption.
This allows for parameterized placement (including movement) of foreground and background sound sources.
SpatialScaper also includes data augmentation pipelines that can be applied to existing SELD data.
As a case study, we use SpatialSCaper to add rooms to the DCASE SELD data.
Training a model with our data led to progressive performance improves as a direct function of acoustic diversity.
These results show that SpatialScaper is valuable to train robust SELD models.