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"en": "DESCRIPTION: This audio dataset serves serves as supplementary material for the DCASE2022 Challenge Task 3: Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the TAU-NIGENS Spatial Sound Scenes 2021 dataset for the DCASE2021 Challenge Task 3. The SELD task setup in DCASE2022 is based on spatial recordings of real scenes, captured in the STARS22 dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the baseline model, and they are shared for reasons of reproducibility. For more details on the task setup, please refer to the task description. Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see: 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. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021), Barcelona, Spain. available here. SPECIFICATIONS: 13 target sound classes (see task description for details) The sound event samples are sources from the FSD50K dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here. 1200 1-minute long spatial recordings Sampling rate of 24kHz Two 4-channel recording formats, first-order Ambisonics (FOA) and tetrahedral microphone array (MIC) Spatial events spatialized in 9 unique rooms, using measured RIRs for the two formats Maximum polyphony of 2 (with possible same-class events overlapping) Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a separation into a training and testing split, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only). The training split is indicated as fold1 in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K. The testing split is indicated as fold2 in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K. Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup. FSD50K SELECTION: The list of selected sound event recordings is included along the recordings and metadata, as FSD50K_selected.txt. Each line in the text file has the following structure: [target_label]/[train/test]/[FSD50K_label]/filename.wav with an example: domesticSounds/train/Boiling/16584.wav meaning that the file 16584.wav from FSD50K, with the Boiling label of FSD50K, is included in the samples for the training split of those synthetic recordings, and it is mapped to the target class of domestic sounds. Note that there can be multiple FSD50K labels mapped the same target class. Also note that if these are downloaded from FSD50K, and a folder structure is created that replicates the structure in the list, the resulting folder can be used out-of-the-box with the scene generator to generate new mixtures with the same or different parameters. Note that no sounds form FSD50K have been selected for the Music target class. Background and pop music tracks from the public domain have been cropped and used instead. DOWNLOAD INSTRUCTIONS: Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal: Combine the split archive to a single archive: zip -s 0 split.zip --out single.zip Extract the single archive using unzip: unzip single.zip"
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"en": "DESCRIPTION The TAU Spatial Room Impulse Response Database (TAU-SRIR DB) database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are: Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural). Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios. Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods. Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms. The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND. NOTE: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository. REPORT AND REFERENCE A compact description of the dataset, recording setup, recording procedure, and extraction can be found in: Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (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. available here. A more detailed report specifically focusing on the dataset collection and properties will follow. AIM The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios: monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions monophonic and multichannel polyphonic sound events in multi-room reverberant conditions single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios SPECIFICATIONS The SRIRs were captured using an [Eigenmike](https://mhacoustics.com/products) spherical microphone array. A [Genelec G Three loudspeaker](https://www.genelec.com/g-three) was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness. The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\\phi\\in[-180,180)$, while the elevations span approximately a range between $\\theta\\in[-45,45]$ degrees. The currently shared measured spaces are as follows: Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory. Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory. Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory. Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory. Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory. Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory. Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory. Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory. Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory. The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a close and a far one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, close and far, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room. Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights. The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier. More details on the trajectory geometries can be found in the README file and the measinfo.mat file. RECORDING FORMATS As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README. We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural). REFERENCE DOAs For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal. The DOAs are provided as Cartesian components [x, y, z] of unit length vectors. SCENE GENERATOR A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the TAU-NIGENS Spatial Sound Events 2021 dataset, and has been ported to Python from the original version written in Matlab. The generator can be found [**here**](https://github.com/danielkrause/DCASE2022-data-generator), along with more details on its use. The generator at the moment is set to work with the NIGENS sound event sample database, and the FSD50K sound event database, but additional sample banks can be added with small modifications. The dataset together with the generator has been used by the authors in the following public challenges: - DCASE 2019 Challenge Task 3, to generate the TAU Spatial Sound Events 2019 dataset (development/evaluation) - DCASE 2020 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2020 dataset - DCASE2021 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2021 dataset - DCASE2022 Challenge Task 3, to generate additional SELD synthetic mixtures for training the task baseline NOTE: The current version of the generator is work-in-progress, with some code being quite \"rough\". If something does not work as intended or it is not clear what certain parts do, please contact us. DATASET STRUCTURE The dataset contains a folder of the SRIRs (TAU-SRIR_DB), with all the SRIRs per room in a single MAT file. The file rirdata.mat contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file measinfo.mat contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (TAU-SNoise_DB), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README. DOWNLOAD The files TAU-SRIR_DB.z01, ..., TAU-SRIR_DB.zip contain the SRIRs and measurement info files. The files TAU-SNoise_DB.z01, ..., TAU-SNoise_DB.zip contain the ambient noise recordings. 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REPORT & REFERENCE: If you use this dataset please cite the report on its creation, and the related DCASE2022 task setup: 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. arXiv preprint arXiv:2206.01948. found here. AIM: The 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. SPECIFICATIONS: 70 recording clips of 30 sec ~ 5 min durations, with a total time of ~2hrs, contributed by SONY (development dataset). 51 recording clips of 1 min ~ 5 min durations, with a total time of ~3hrs, contributed by TAU (development dataset). 52 recording clips with a total time of ~2hrs, contributed by SONY&TAU (evaluation dataset). A training-test 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). 27 recordings contributed by TAU for the training split, captured in 4 rooms (dev-train-tau). 24 recordings contributed by TAU for the testing split, captured in 3 rooms (dev-test-tau). A total of 11 unique rooms captured in the recordings, 4 from SONY and 7 from TAU (development set). Sampling rate 24kHz. Two 4-channel 3-dimensional recording formats: first-order Ambisonics (FOA) and tetrahedral microphone array (MIC). Recordings are taken in two different countries and 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. Occurences of up to 3 simultaneous events are fairly common, while higher numbers of overlapping events (up to 5) can occur but are rare. 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 in the DCASE 2022 Sound Event Localization and Detection Task. DEVELOPMENT AND EVALUATION: The current version (Version 1.1) of the dataset includes the 121 development audio recordings and labels, used by the participants of Task 3 of DCASE2022 Challenge to train and validate their submitted systems, and the 52 evaluation audio recordings without labels, for the evaluation phase of DCASE2022. If researchers wish to compare their system against the submissions of DCASE2022 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 metadata_dev.zip is the common metadata for both formats. The file foa_eval.zip, corresponds to audio data of the FOA recording format for the evaluation dataset. 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