The Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset contains multichannel recordings of sound scenes in various rooms and environments, together with temporal and spatial annotations of prominent events belonging to a set of target classes. The dataset is collected in two different countries, in Tampere, Finland by the Audio Researh Group (ARG) of Tampere University (TAU), and in Tokyo, Japan by SONY, using a similar setup and annotation procedure. The dataset is delivered in two 4-channel spatial recording formats, a microphone array one (MIC), and first-order Ambisonics one (FOA). These recordings serve as the development dataset for the DCASE 2023 Sound Event Localization and Detection Task of the DCASE 2023 Challenge.

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. Source: STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events Image https://arxiv.org/pdf/2206.01948v2.pdf

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. ## Scope of the Challenge The L3DAS22 Challenge aims at encouraging and fostering research on machine learning for 3D audio signal processing. 3D audio is gaining increasing interest in the machine learning community in recent years. The range of applications is incredibly wide, extending from virtual and real conferencing to autonomous driving, surveillance and many more. In these contexts, a fundamental procedure is to properly identify the nature of events present in a soundscape, their spatial position and eventually remove unwanted noises that can interfere with the useful signal. To this end, L3DAS22 Challenge presents two tasks: 3D Speech Enhancement and 3D Sound Event Localization and Detection, both relying on first-order Ambisonics recordings in reverberant office environments. Each task involves 2 separate tracks: 1-mic and 2-mic recordings, respectively containing sounds acquired by one 1st order Ambisonics microphone and by an array of two ones. The use of two Ambisonics microphones represents one of the main novelties of the L3DAS22 Challenge. We expect higher accuracy/reconstruction quality when taking advantage of the dual spatial perspective of the two microphones. Moreover, we are very interested in identifying other possible advantages of this configuration over standard Ambisonics formats. Interactive demos of our baseline models are available on Replicate. Top 5 ranked teams can submit a regular paper according to the ICASSP guidelines. Prizes will be awarded to the challenge winners thanks to the support of Kuaishou Technology. ## Tasks Tasks The tasks we propose are: * 3D Speech Enhancement The objective of this task is the enhancement of speech signals immersed in the spatial sound field of a reverberant office environment. Here the models are expected to extract the monophonic voice signal from the 3D mixture containing various background noises. The evaluation metric for this task is a combination of short-time objective intelligibility (STOI) and word error rate (WER). * 3D Sound Event Localization and Detection The aim of this task is to detect the temporal activities of a known set of sound event classes and, in particular, to further locate them in the space. Here the models must predict a list of the active sound events and their respective location at regular intervals of 100 milliseconds. Performance on this task is evaluated according to the location-sensitive detection error, which joins the localization and detection error metrics. ## Dataset Info The L3DAS22 datasets contain multiple-source and multiple-perspective B-format Ambisonics audio recordings. We sampled the acoustic field of a large office room, placing two first-order Ambisonics microphones in the center of the room and moving a speaker reproducing the analytic signal in 252 fixed spatial positions. Relying on the collected Ambisonics impulse responses (IRs), we augmented existing clean monophonic datasets to obtain synthetic tridimensional sound sources by convolving the original sounds with our IRs. We extracted speech signals from the Librispeech dataset and office-like background noises from the FSD50K dataset. We aimed at creating plausible and variegate 3D scenarios to reflect possible real-life situations in which sound and disparate types of background noises coexist in the same 3D reverberant environment. We provide normalized raw waveforms as predictors data and the target data varies according to the task. The dataset is divided in two main sections, respectively dedicated to the challenge tasks. The first section is optimized for 3D Speech Enhancement and contains more than 60000 virtual 3D audio environments with a duration up to 12 seconds. In each sample, a spoken voice is always present alongside with other office-like background noises. As target data for this section we provide the clean monophonic voice signals. For each subset we also provide a csv file, where we annotated the coordinates and spatial distance of the IR convolved with the target voice signals for each datapoint. This may be useful to estimate the delay caused by the virtual time-of-flight of the target voice signal and to perform a sample-level alignment of the input and ground truth signals. The other sections, instead, is dedicated to the 3D Sound Event Localization and Detection task and contains 900 30-seconds-long audio files. Each data point contains a simulated 3D office audio environment in which up to 3 simultaneous acoustic events may be active at the same time. In this section, the samples are not forced to contain a spoken voice. As target data for this section we provide a list of the onset and offset time stamps, the typology class, and the spatial coordinates of each individual sound event present in the data-points. We split both dataset sections into a training set and a development set, paying attention to create similar distributions. The train set of the SE section is divided in two partitions: train360 and train100, and contain speech samples extracted from the correspondent partitions of Librispeech (only the sample) up to 12 seconds. The train360 is split in 2 zip files for a more convenient download. All sets of the SELD section are divided in: OV1, OV2, OV3. These partitions refer to the maximum amount of possible overlapping sounds, which are 1, 2 or 3, respectively. ## L3DAS22 Challenge Supporting API The gitHub supporting API is aimed at downloading the dataset, pre-processing the sound files and the metadata, training and evaluating the baseline models and validating the final results. We provide easy-to-use instruction to produce the results included in our paper. Moreover, we extensively commented our code for easy customization. For further information please refer to the challenge website and to the challenge documentation.

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. The LEDAS21 datasets contain multiple-source and multiple-perspective B-format Ambisonics audio recordings. The authors sampled the acoustic field of a large office room, placing two first-order Ambisonics microphones in the center of the room and moving a speaker reproducing the analytic signal in 252 fixed spatial positions. Relying on the collected Ambisonics impulse responses (IRs), the authors augmented existing clean monophonic datasets to obtain synthetic tridimensional sound sources by convolving the original sounds with our IRs. The dataset is divided in two main sections, respectively dedicated to the challenge tasks. The first section is optimized for 3D Speech Enhancement and contains more than 30000 virtual 3D audio environments with a duration up to 10 seconds. In each sample, a spoken voice is always present alongside with other office-like background noises. As target data for this section the authors provide the clean monophonic voice signals. The other sections, instead, is dedicated to the 3D Sound Event Localization and Detection task and contains 900 60-seconds-long audio files. Each data point contains a simulated 3D office audio environment in which up to 3 simultaneous acoustic events may be active at the same time. In this section, the samples are not forced to contain a spoken voice. As target data for this section the authors provide a list of the onset and offset time stamps, the typology class, and the spatial coordinates of each individual sound event present in the data-points. Source: L3DAS21

The PodcastFillers dataset consists of 199 full-length podcast episodes in English with manually annotated filler words and automatically generated transcripts. The podcast audio recordings, sourced from SoundCloud, are CC-licensed, gender-balanced, and total 145 hours of audio from over 350 speakers. The annotations are provided under a non-commercial license and consist of 85,803 manually annotated audio events including approximately 35,000 filler words (“uh” and “um”) and 50,000 non-filler events such as breaths, music, laughter, repeated words, and noise. The annotated events are also provided as pre-processed 1-second audio clips. The dataset also includes automatically generated speech transcripts from a speech-to-text system. A detailed description is provided in Dataset.

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 the synthesis of the sound scenes are obtained from the NIGENS general sound events database. These recordings serve as the development dataset for the DCASE 2021 Sound Event Localization and Detection Task of the DCASE 2021 Challenge.

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 synthesis of the sound scenes are obtained from the NIGENS general sound events database. These recordings serve as the development dataset for the DCASE 2020 Sound Event Localization and Detection Task of the DCASE 2020 Challenge.

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).

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.