STARSS23: Sony-TAu Realistic Spatial Soundscapes 2023

DESCRIPTION: 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 STARSS23 dataset is a continuation of the STARSS22 dataset. It extends the previous version with the following: An additional 2.5hrs of recordings in the development set, from 5 new rooms distributed in 47 new recording clips. Distance labels (in cm) for the spatially annotated sound events, apart from only the previous azimuth and elevation labels. 360° videos 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. Collection of data from the TAU side has received funding from Google. REPORT & REFERENCE: 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). 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. 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. AIM: 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. SPECIFICATIONS: 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 (fold3_room21_mix001.wav - fold3_room21_mix012.wav) More detailed information on the dataset can be found in the included README file. SOUND CLASSES: 13 target sound event classes are annotated. The classes follow loosely the Audioset ontology. 0. Female speech, woman speaking 1. Male speech, man speaking 2. Clapping 3. Telephone 4. Laughter 5. Domestic sounds 6. Walk, footsteps 7. Door, open or close 8. Music 9. Musical instrument 10. Water tap, faucet 11. Bell 12. Knock 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. EXAMPLE APPLICATION: 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. A baseline for the audiovisual track of DCASE 2023 Sound Event Localization and Detection Task will be published soon and referenced here. DEVELOPMENT AND EVALUATION: 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. DOWNLOAD INSTRUCTIONS: The file foa_dev.zip, correspond to audio data of the FOA recording format. The file mic_dev.zip, correspond to audio data of the MIC recording format. The file video_dev.zip contains the common videos for both audio formats. The file metadata_dev.zip 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.数据集概述： 索尼-TAU真实空间声场2023（Sony-TAu Realistic Spatial Soundscapes 2023, STARSS23）数据集包含多种房间与环境下的多声道声景录音，以及属于若干目标类别的显著事件的时空标注。该数据集由两处机构采集：芬兰坦佩雷大学（Tampere University, TAU）的音频研究组（Audio Researh Group, ARG）在坦佩雷采集，日本索尼（SONY）在东京采集，两处采用了一致的采集设置与标注流程。数据集以两种4声道空间录音格式提供：麦克风阵列格式（MIC）与一阶 Ambisonics格式（FOA）。本数据集作为DCASE 2023挑战赛声音事件定位与检测任务的开发数据集使用，是STARSS22数据集的延续。 STARSS23相较于前代版本新增了以下内容：开发集新增2.5小时录音，来自47个新录音片段、分布于5个新房间；为空间标注的声音事件新增距离标签（单位：厘米），此前仅包含方位角与仰角标签；提供360°视频，与数据集的音频录音实现时空对齐（仅12个纯音频片段除外）。后续版本将在数据集的评估集中新增更多音频与视频录音。 与此前DCASE挑战赛历届关联的TAU 2019空间声事件（开发/评估集）、TAU-NIGENS 2020空间声事件、TAU-NIGENS 2021空间声事件这三个合成空间声景数据集不同，STARSS22-23数据集包含真实声景录音，因此规避了合成声景生成的部分缺陷。其关键特性包括：标注结合了人工标注的声音事件活动与光学追踪的空间位置；标注的目标事件类别由真实声景的构成决定；事件与声音类别的密度、复音性、出现次数及共现次数并非随机，而是遵循真实场景中参与者的行为与交互。首轮录音采集于2021年9月至2022年1月，第二轮采集于2022年11月至2023年2月。坦佩雷大学一侧的数据采集工作获得了谷歌（Google）的资助。 报告与参考文献： 若您使用本数据集，请引用以下关于其设计、采集与标注流程的报告：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. 相关链接详见此处。关于新数据集特性及其音视频处理的更详细报告，以及适配DCASE2023的基线模型，将于近期发布。 应用场景： STARSS22-23数据集适用于训练与评估用于声音事件检测（SED）、针对多样声音或目标信号的通用声源定位、以及联合声音事件定位与检测（SELD）的机器学习模型。此外，本数据集可用于评估无需依赖训练的信号处理方法，例如声源定位方法与多声源声学追踪方法。该数据集可评估上述应用在多样声音类型与多样声学条件下的性能与鲁棒性。具体而言，STARSS23还可用于评估音视频处理方法，例如音视频声源定位。 数据集规格： 通用说明：录音采集于两个不同站点。每个录音片段均属于在唯一房间中进行的录音会话，每组参与者、发声道具与场景方案均为每个会话独有（少数例外）。为实现声音事件在存在性、密度、运动及/或空间分布上的多样性与采集效率，场景采用松散脚本化设计。数据集中共标注13个目标类别，并由人工完成精细标注。活跃事件的空间标注由光学追踪系统获取。非目标类别的声音事件被视为干扰信号。同时出现最多3个事件的情况较为常见，而同时出现更多事件（最多5个）的情况虽偶有发生但较为罕见。 音量、时长与数据划分：开发集中共采集了16个独特房间的录音，其中东京站点4个，坦佩雷站点12个。东京站点采集了70个录音片段，时长介于30秒至5分钟之间，总时长约2小时；坦佩雷站点采集了98个录音片段，时长介于40秒至9分钟之间，总时长约5.5小时。开发集提供训练-测试划分方案用于结果报告： - 索尼贡献的训练集录音共40个，采集于2个房间（dev-train-sony） - 索尼贡献的测试集录音共30个，采集于2个房间（dev-test-sony） - 坦佩雷大学贡献的训练集录音共50个，采集于7个房间（dev-train-tau） - 坦佩雷大学贡献的测试集录音共48个，采集于5个房间（dev-test-tau） 两处站点额外采集的约3.5小时录音，来自开发集之外的不同房间，将在后续作为评估集发布。 音频参数： 采样率：24kHz。两种4声道三维录音格式：一阶 Ambisonics（FOA）与四面体麦克风阵列（MIC）。 视频参数： 360°视频格式：等距柱状投影；视频分辨率：1920×960；视频帧率（fps）：29.97。除12个音频录音（文件名为fold3_room21_mix001.wav至fold3_room21_mix012.wav）缺失视频外，所有音频录音均配有同步视频录音。更多数据集详细信息可参阅附带的README文件。 声音类别： 数据中共标注13个目标声音事件类别，类别框架大致遵循Audioset本体论： 0. 女性语音：女性讲话 1. 男性语音：男性讲话 2. 拍手 3. 电话声响 4. 笑声 5. 室内日常声响 6. 行走、脚步声 7. 门开关声 8. 音乐 9. 乐器声 10. 水龙头水流声 11. 铃声 12. 敲击声 部分类别的内容对应Audioset相关子类的有限范围，更多信息详见README文件。 示例应用： 本数据集提供了一个卷积循环神经网络的可训练模型实现，用于执行联合SELD任务，基于本数据集进行训练与评估。该实现将作为DCASE 2023挑战赛声音事件定位与检测任务音频赛道的基线方法。适配DCASE 2023挑战赛声音事件定位与检测任务音视频赛道的基线模型，将于近期发布并在此处引用。 开发与评估： 当前版本（V1.0）的数据集仅包含168个开发集音视频录音与标注，供DCASE2023挑战赛任务3的参赛者训练与验证其提交的系统。V1.1版本将额外包含评估集音视频录音（无标注），用于DCASE2023的评估阶段。若研究人员希望将其系统与DCASE2023挑战赛的提交结果进行对比，使用评估数据作为测试集即可获得直接可比的结果。 下载说明： 文件foa_dev.zip对应FOA录音格式的音频数据；文件mic_dev.zip对应MIC录音格式的音频数据；文件video_dev.zip包含两种音频格式共用的视频文件；文件metadata_dev.zip包含两种音频格式共用的元数据。请下载对应所需格式的压缩包，并使用任意解压工具进行解压。