The Sony-TAu Realistic Spatial Soundscapes 2022(STARSS22) dataset consists of recordings of real scenes captured with high channel-count spherical microphone array (SMA). The recordings are conducted from two different teams at two different sites, Tampere University in Tammere, Finland, and Sony facilities in Tokyo, Japan. Recordings at both sites share the same capturing and annotation process, and a similar organization. They are organized in sessions, corresponding to distinct rooms, human participants, and sound making props with a few exceptions.
17 PAPERS • 1 BENCHMARK
L3DAS22: MACHINE LEARNING FOR 3D AUDIO SIGNAL PROCESSING This dataset supports the L3DAS22 IEEE ICASSP Gand Challenge. The challenge is supported by a Python API that facilitates the dataset download and preprocessing, the training and evaluation of the baseline models and the results submission.
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L3DAS21 is a dataset for 3D audio signal processing. It consists of a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage.
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The TAU-NIGENS Spatial Sound Events 2021 dataset contains multiple spatial sound-scene recordings, consisting of sound events of distinct categories integrated into a variety of acoustical spaces, and from multiple source directions and distances as seen from the recording position. The spatialization of all sound events is based on filtering through real spatial room impulse responses (RIRs), captured in multiple rooms of various shapes, sizes, and acoustical absorption properties. Furthermore, each scene recording is delivered in two spatial recording formats, a microphone array one (MIC), and first-order Ambisonics one (FOA). The sound events are spatialized as either stationary sound sources in the room, or moving sound sources, in which case time-variant RIRs are used. Each sound event in the sound scene is associated with a single direction-of-arrival (DoA) if static, a trajectory DoAs if moving, and a temporal onset and offset time. The isolated sound event recordings used for t
4 PAPERS • 1 BENCHMARK
The TAU-NIGENS Spatial Sound Events 2020 dataset contains multiple spatial sound-scene recordings, consisting of sound events of distinct categories integrated into a variety of acoustical spaces, and from multiple source directions and distances as seen from the recording position. The spatialization of all sound events is based on filtering through real spatial room impulse responses (RIRs), captured in multiple rooms of various shapes, sizes, and acoustical absorption properties. Furthermore, each scene recording is delivered in two spatial recording formats, a microphone array one (MIC), and first-order Ambisonics one (FOA). The sound events are spatialized as either stationary sound sources in the room, or moving sound sources, in which case time-variant RIRs are used. Each sound event in the sound scene is associated with a trajectory of its direction-of-arrival (DoA) to the recording point, and a temporal onset and offset time. The isolated sound event recordings used for the sy
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We recorded gun sounds by changing the type and position of guns to diversify distances and angles in the PUBG environment. The BGG dataset consists of 2,195 samples with 37 different types of guns and five directions, including a silence in which there is no gunfire, but noises exist. The distance from the firearms ranged from 0 meters to 600 meters. The audio was recorded in stereo (i.e., two-channel audio), and each sample contains various environmental noises (e.g., water splashing, walking, and bullet friction).
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The RWCP Sound Scene Database includes non-speech sounds recorded in an anechoic room, reconstructed signals in various rooms, impulse responses for a microphone array, speech data recorded with the same array, and recordings of background noises. It is intended for use when simulating sound scenes. It was developed by the Real Acoustic Environments Working Group of the Real World Computing Partnership (RWCP). The data was recorded from 1998 to 2000.
1 PAPER • 1 BENCHMARK