DOI: 10.5281/zenodo.7709052
title: STARSS23: Sony-TAu Realistic Spatial Soundscapes 2023

description: <strong>DESCRIPTION:</strong> The <strong>Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23)</strong> 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 <strong>Tampere University (TAU)</strong>, and in Tokyo, Japan by <strong>SONY</strong>, using a similar setup and annotation procedure. The dataset is delivered in two 4-channel spatial recording formats, a microphone array one (<strong>MIC</strong>), and first-order Ambisonics one (<strong>FOA</strong>). These recordings serve as the development dataset for the DCASE 2023 Sound Event Localization and Detection Task of the DCASE 2023 Challenge.<br> <br> The STARSS23 dataset is a continuation of the STARSS22 dataset. It extends the previous version with the following: An additional <strong>2.5hrs </strong>of recordings in the development set, from <strong>5 new rooms</strong> distributed in 47 new recording clips. <strong>Distance labels</strong> (in cm) for the spatially annotated sound events, apart from only the previous azimuth and elevation labels. <strong>360° videos</strong> spatially and temporally aligned to the audio recordings of the dataset (apart from 12 audio-only clips). Additional new audio and video recordings will be added in the evaluation set of the dataset in a subsequent version. Contrary to the three previous datasets of synthetic spatial sound scenes of TAU Spatial Sound Events 2019 (development/evaluation), TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021 associated with previous iterations of the DCASE Challenge, the STARS22-23 dataset contains recordings of real sound scenes and hence it avoids some of the pitfalls of synthetic generation of scenes. Some such key properties are: annotations are based on a combination of human annotators for sound event activity and optical tracking for spatial positions, the annotated target event classes are determined by the composition of the real scenes, the density, polyphony, occurences and co-occurences of events and sound classes is not random, and it follows actions and interactions of participants in the real scenes. The first round of recordings was collected between September 2021 and January 2022. A second round of recordings was collected between November 2022 and February 2023.<br> <br> Collection of data from the TAU side has received funding from Google. <strong>REPORT & REFERENCE:</strong> If you use this dataset you could cite this report on its design, capturing, and annotation process: Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen (2022). <strong>STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events</strong>. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022)</em>, Nancy, France. found here. A more detailed report on the properties of the new dataset and its audiovisual processing with a suitable baseline for DCASE2023 will be published soon. <strong>AIM:</strong> The STARSS22-23 dataset is suitable for training and evaluation of machine-listening models for sound event detection (SED), general sound source localization with diverse sounds or signal-of-interest localization, and joint sound-event-localization-and-detection (SELD). Additionally, the dataset can be used for evaluation of signal processing methods that do not necessarily rely on training, such as acoustic source localization methods and multiple-source acoustic tracking. The dataset allows evaluation of the performance and robustness of the aforementioned applications for diverse types of sounds, and under diverse acoustic conditions. Specifically the STARSS23 allows additionally evaluation of audiovisual processing methods, such as audiovisual source localization. <strong>SPECIFICATIONS:</strong> General: Recordings are taken in two different sites. Each recording clip is part of a recording session happening in a unique room. Groups of participants, sound making props, and scene scenarios are unique for each session (with a few exceptions). To achieve good variability and efficiency in the data, in terms of presence, density, movement, and/or spatial distribution of the sounds events, the scenes are loosely scripted. 13 target classes are identified in the recordings and strongly annotated by humans. Spatial annotations for those active events are captured by an optical tracking system. Sound events out of the target classes are considered as interference. Occurrences of up to 3 simultaneous events are fairly common, while higher numbers of overlapping events (up to 5) can occur but are rare. Volume, duration, and data split: A total of 16 unique rooms captured in the recordings, 4 in Tokyo and 12 in Tampere (development set). 70 recording clips of 30 sec ~ 5 min durations, with a total time of ~2hrs, captured in Tokyo (development dataset). 98 recording clips of 40 sec ~ 9 min durations, with a total time of ~5.5hrs, captured in Tampere (development dataset). A training-testing split is provided for reporting results using the development dataset. 40 recordings contributed by Sony for the training split, captured in 2 rooms (dev-train-sony). 30 recordings contributed by Sony for the testing split, captured in 2 rooms (dev-test-sony). 50 recordings contributed by TAU for the training split, captured in 7 rooms (dev-train-tau). 48 recordings contributed by TAU for the testing split, captured in 5 rooms (dev-test-tau). About ~3.5hrs of additional recordings from both sites, captured in different rooms from the development set, will be released later as the evaluation set. Audio: Sampling rate: 24kHz. Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array (MIC). Video: Video 360° format: equirectangular Video resolution: 1920x960 Video frames per second (fps): 29.97 All audio recordings are accompanied by synchronised video recordings, apart from 12 audio recordings with missing videos (<em>fold3_room21_mix001.wav - fold3_room21_mix012.wav</em>) More detailed information on the dataset can be found in the included README file. <strong>SOUND CLASSES:</strong> 13 target sound event classes are annotated. The classes follow loosely the Audioset ontology. 0. <strong>Female speech, woman speaking</strong><br> 1. <strong>Male speech, man speaking</strong><br> 2. <strong>Clapping</strong><br> 3. <strong>Telephone</strong><br> 4. <strong>Laughter</strong><br> 5. <strong>Domestic sounds</strong><br> 6. <strong>Walk, footsteps</strong><br> 7. <strong>Door, open or close</strong><br> 8. <strong>Music</strong><br> 9. <strong>Musical instrument</strong><br> 10. <strong>Water tap, faucet</strong><br> 11. <strong>Bell</strong><br> 12. <strong>Knock</strong> The content of some of these classes corresponds to events of a limited range of Audioset-related subclasses. For more information see the README file. <strong>EXAMPLE APPLICATION:</strong> An implementation of a trainable model of a convolutional recurrent neural network, performing joint SELD, trained and evaluated with this dataset is provided here. This implementation will serve as the baseline method for the audio-only track in the DCASE 2023 Sound Event Localization and Detection Task.<br> <br> A baseline for the audiovisual track of DCASE 2023 Sound Event Localization and Detection Task will be published soon and referenced here. <strong>DEVELOPMENT AND EVALUATION:</strong> The current version (Version 1.0) of the dataset includes only the 168 development audio/video recordings and labels, used by the participants of Task 3 of DCASE2023 Challenge to train and validate their submitted systems. Version 1.1 will be including additionally the evaluation audio and video recordings without labels, for the evaluation phase of DCASE2023. If researchers wish to compare their system against the submissions of DCASE2023 Challenge, they will have directly comparable results if they use the evaluation data as their testing set. <strong>DOWNLOAD INSTRUCTIONS:</strong> The file <strong><em>foa_dev.zip</em></strong>, correspond to audio data of the <strong>FOA </strong>recording format.<br> The file <strong><em>mic_dev.zip</em></strong>, correspond to audio data of the <strong>MIC</strong> recording format. The file <strong><em>video_dev.zip </em></strong>contains the common videos for both audio formats.<br> The file <strong><em>metadata_dev.zip</em></strong> contains the common metadata for both audio formats. Download the zip files corresponding to the format of interest and use your favourite compression tool to unzip these zip files.
description: {"references": ["Archontis Politis, Kazuki Shimada, Parthasaarathy Sudarsanam, Sharath Adavanne, Daniel Krause, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji, Tuomas Virtanen (2022). STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), Nancy, France.", "Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection.\u00a0 In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), Barcelona, Spain.", "Archontis Politis, Sharath Adavanne, and Tuomas Virtanen (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan.", "Sharath Adavanne, Archontis Politis, and Tuomas Virtanen (2019). A Multi-room reverberant dataset for sound event localization and detection. Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), New York, NY, USA."]}