  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.
