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This report presents the Sony-TAu Realistic Spatial Soundscapes 2022(STARS22) dataset for sound event localization and detection, comprised ofspatial recordings of real scenes collected in various interiors of twodifferent sites. The dataset is captured with a high resolution sphericalmicrophone array and delivered in two 4-channel formats, first-order Ambisonicsand tetrahedral microphone array. Sound events in the dataset belonging to 13target sound classes are annotated both temporally and spatially through acombination of human annotation and optical tracking. The dataset serves as thedevelopment and evaluation dataset for the Task 3 of the DCASE2022 Challenge onSound Event Localization and Detection and introduces significant newchallenges for the task compared to the previous iterations, which were basedon synthetic spatialized sound scene recordings. Dataset specifications aredetailed including recording and annotation process, target classes and theirpresence, and details on the development and evaluation splits. Additionally,the report presents the baseline system that accompanies the dataset in thechallenge with emphasis on the differences with the baseline of the previousiterations; namely, introduction of the multi-ACCDOA representation to handlemultiple simultaneous occurences of events of the same class, and support foradditional improved input features for the microphone array format. Results ofthe baseline indicate that with a suitable training strategy a reasonabledetection and localization performance can be achieved on real sound scenerecordings. The dataset is available in https://zenodo.org/record/6387880.

STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events