This dataset was recorded and designed for DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes (S5). The dataset contains 18 classes of sound events recorded in an anechoic chamber (ASE1K) and room impulse responses (RIRs) recorded using first-order ambisonics (FOA) microphones. All of the acoustic data and RIR formats included in this dataset are 32kHz/16bit. In the following part of this description, we will briefly summarize the recording of sound events and RIR. Anechoic Sound Event (ASE) 1K ASE1K is a one-shot recording of 18 classes of sound events recorded in an anechoic chamber. The recording was made using three cardioid microphones to capture the sound events from the left, front and right, and one omnidirectional microphone to capture the sound from above. In the S5 task, it is assumed that you will simply select a single channel (e.g. ch=3) from these and use it as a monaural sound event. For each class, 50 to 80 events were recorded, and a total of over 1K samples were recorded. FOA RIR for DCASE2025 Task4 + FOA-MEIR The RIR dataset is made up of RIRs recorded in six environments for DCASE2025 Task4, combined with RIRs that have already been released as the FOA-MEIR dataset. All recordings were made using the same FOA Microphone (Sennheiser Ambeo VR Mic). RIR recordings were made from multiple locations in each environment, and these are compiled in sofa file format. Noise recordings FOA-MEIR This dataset also includes noise recordings in the FOA format. All of noise recordings are all the same as those included in FOA-MEIR. The recordings were made using the same FOA microphones as those used in RIR. Further information is available at DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes (S5), Github, and [1] [1] Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada, Romain Serizel, Mayank Mishra, Marc Delcroix, Shoko Araki, Daiki Takeuchi, Daisuke Niizumi, Yasunori Ohishi, Tomohiro Nakatani, Takao Kawamura, Nobutaka Ono, "Description and discussion on DCASE 2025 challenge task 4: Spatial Semantic Segmentation of Sound Scenes," in arXiv preprint arXiv:xxxx.xxxx, 2025. (will be available soon) License: see the file named LICENSEv2.2.pdf