DCASE 2016 is a dataset for sound event detection. It consists of 20 short mono sound files for each of 11 sound classes (from office environments, like clearthroat, drawer, or keyboard), each file containing one sound event instance. Sound files are annotated with event on- and offset times, however silences between actual physical sounds (like with a phone ringing) are not marked and hence “included” in the event. Source: The NIGENS General Sound Events Database Image Source: https://arxiv.org/pdf/1911.06878.pdf

The FSDnoisy18k dataset is an open dataset containing 42.5 hours of audio across 20 sound event classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. The audio content is taken from Freesound, and the dataset was curated using the Freesound Annotator. The noisy set of FSDnoisy18k consists of 15,813 audio clips (38.8h), and the test set consists of 947 audio clips (1.4h) with correct labels. The dataset features two main types of label noise: in-vocabulary (IV) and out-of-vocabulary (OOV). IV applies when, given an observed label that is incorrect or incomplete, the true or missing label is part of the target class set. Analogously, OOV means that the true or missing label is not covered by those 20 classes. Source: Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers Image Source: http://www.eduardofonseca.net/FSDnoisy18k/

The DESED dataset is a dataset designed to recognize sound event classes in domestic environments. The dataset is designed to be used for sound event detection (SED, recognize events with their time boundaries) but it can also be used for sound event tagging (SET, indicate presence of an event in an audio file). The dataset is composed of 10 event classes to recognize in 10 second audio files. The classes are: Alarm/bell/ringing, Blender, Cat, Dog, Dishes, Electric shaver/toothbrush, Frying, Running water, Speech, Vacuum cleaner. Source: https://project.inria.fr/desed/ Image Source: https://project.inria.fr/desed/

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.

DCASE 2013 is a dataset for sound event detection. It consists of audio-only recordings where individual sound events are prominent in an acoustic scene. Source: http://dcase.community/challenge2013/index Image Source: https://link.springer.com/article/10.1186/s13636-018-0138-4

The TUT Sound Events 2017 dataset contains 24 audio recordings in a street environment and contains 6 different classes. These classes are: brakes squeaking, car, children, large vehicle, people speaking, and people walking. Source: Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling Image Source: https://hal.inria.fr/hal-02067935/document

TUT-SED Synthetic 2016 contains of mixture signals artificially generated from isolated sound events samples. This approach is used to get more accurate onset and offset annotations than in dataset using recordings from real acoustic environments where the annotations are always subjective. Mixture signals in the dataset are created by randomly selecting and mixing isolated sound events from 16 sound event classes together. The resulting mixtures contains sound events with varying polyphony. All together 994 sound event samples were purchased from Sound Ideas. From the 100 mixtures created, 60% were assigned for training, 20% for testing and 20% for validation. The total amount of audio material in the dataset is 566 minutes. Different instances of the sound events are used to synthesize the training, validation and test partitions. Mixtures were created by randomly selecting event instance and from it, randomly, a segment of length 3-15 seconds. Between events, random length silent region was introduced. Such tracks were created for four to nine event classes, and were then mixed together to form the mixture signal. As sound events are not consistently active during the samples (e.g. footsteps), automatic signal energy based annotation was applied to obtain accurate event activity within the sample. Annotation of the mixture signal was created by pooling together event activity annotation of used samples. Source: https://webpages.tuni.fi/arg/paper/taslp2017-crnn-sed/tut-sed-synthetic-2016 Image Source: https://arxiv.org/abs/1702.06286

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 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 BirdVox-full-night dataset contains 6 audio recordings, each about ten hours in duration. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10. Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours. Source: https://wp.nyu.edu/birdvox/birdvox-full-night/ Image Source: https://wp.nyu.edu/birdvox/birdvox-full-night/

The DCASE 2017 rare sound events dataset contains isolated sound events for three classes: 148 crying babies (mean duration 2.25s), 139 glasses breaking (mean duration 1.16s), and 187 gun shots (mean duration 1.32s). As with the DCASE 2016 data, silences are not excluded from active event markings in the annotations. While this data set contains many samples per class, there are only three classes Source: The NIGENS General Sound Events Database Image Source: https://arxiv.org/pdf/1911.06878.pdf

This repository contains the SINGA:PURA dataset, a strongly-labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via a number of recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. The taxonomy we used for the labels in this dataset has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. Please refer to our conference paper published in APSIPA 2021 (which is found in this repository as the file "APSIPA.pdf") or download the readme ("Readme.md") for more details regarding the data collection, annotation, and processing methodologies for the creation of the dataset.

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.

URBAN-SED is a dataset of 10,000 soundscapes with sound event annotations generated using the scraper library. The dataset includes 10,000 soundscapes, totals almost 30 hours and includes close to 50,000 annotated sound events. Every soundscape is 10 seconds long and has a background of Brownian noise resembling the typical “hum” often heard in urban environments. Every soundscape contains between 1-9 sound evnts from the following classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren and street_music. The source material for the sound events are the clips from the UrbanSound8K dataset. URBAN-SED comes pre-sorted into three sets: train, validate and test. There are 6000 soundscapes in the training set, generated using clips from folds 1-6 in UrbanSound8K, 2000 soundscapes in the validation set, generated using clips from fold 7-8 in UrbanSound8K, and 2000 soundscapes in the test set, generated using clips from folds 9-10 in UrbanSound8K. Source: http://urbansed.weebly.com/ Image Source: http://urbansed.weebly.com/

VOICe is a dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures are also offered without any background noise. VOICe consists of 1,449 different mixtures of three different sound events: * 1,242 mixtures with background noise of three different categories of acoustic scenes ("vehicle"," outdoors", and "indoors"), mixed under 2 SNR values (-3, -9 dB), that is 207 mixtures x 3 acoustic scenes x 2 SNRs = 1,242 * 207 mixtures without any background noise.

WildDESED is an extension of the original DESED dataset, created to reflect various domestic scenarios by incorporating complex and unpredictable background noises. These enhancements make WildDESED a powerful resource for developing and evaluating noise-robust SED systems.

BIOSED-ACPD: BIOacoustic Sound Event Detection - Adaptive Change Point Detection dataset Description. This dataset contains the generated audio mixtures with strong annotations, and pre-computed BirdNET embeddings used in the paper "From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning". The results of the paper can be reproduced at three different levels using this dataset: results_eusipco_2024.zip : contains the experiment results used to produce all tables and figures in the paper. data.zip : contains the pre-computed BirdNET embeddings, labels and the JAMS files from Scaper. audio_data.tar.gz : contains the pre-generated audio mixtures for the datasets. At level (1) the figures and tables are produced using the exact results from the experiments presented in the paper, at level (2) the pre-computed embeddings and annotations are used to run the experiments and reproduce the results, and at level (3) the embeddings can be computed using BirdNET on the audio mixtures. For the audio mixtures download all parts and run "cat audio_data.tar.gz.* > audio_data.tar.gz". source code : https://github.com/johnmartinsson/adaptive-change-point-detection/blob/main/README.md paper link : https://arxiv.org/abs/2403.08525 Cite as. @misc{martinsson2024weak, title={From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning}, author={John Martinsson and Olof Mogren and Maria Sandsten and Tuomas Virtanen}, year={2024}, eprint={2403.08525}, archivePrefix={arXiv}, primaryClass={cs.SD} } Attribution. The audio mixtures are derived from three openly available audio datasets: Nolasco, I., Singh, S., Strandburg-Peshkin, A., Gill, L., Pamula, H., Grout, E., Morford, J., Emmerson, M., Jensens, F., Whitehead, H., Kiskin, I., Vidana-Vila, E., Lostanlen, V., & Stowell, D. (2022). DCASE 2022 Task 5: Few-shot Bioacoustic Event Detection Evaluation Set [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6517414, available under the Creative Commons Attribution 4.0 International License Trowitzsch, I., Taghia, J., Kashef, Y., & Obermayer, K. (2019). NIGENS general sound events data

This dataset is an audio dataset containing about 1500 audio clips recorded by multiple professional players. Source: Zenodo

USM-SED is a dataset for polyphonic sound event detection in urban sound monitoring use-cases. Based on isolated sounds taken from the FSD50k dataset, 20,000 polyphonic soundscapes are synthesized with sounds being randomly positioned in the stereo panorama using different loudness levels.

The TUT Sounds Event 2018 dataset consists of real-life first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours. The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m. Source: https://zenodo.org/record/1237793 Image Source: https://www.cs.tut.fi/~mesaros/pubs/mesaros_eusipco2016-dcase.pdf