Dataset Open Access
FSD50K is an open dataset of human-labeled sound events containing 51,197 Freesound clips unequally distributed in 200 classes drawn from the AudioSet Ontology. FSD50K has been created at the Music Technology Group of Universitat Pompeu Fabra.
If you use the FSD50K dataset, or part of it, please cite our paper:
Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. "FSD50K: an Open Dataset of Human-Labeled Sound Events", arXiv:2010.00475, 2020.
Eduardo Fonseca, Xavier Favory, Jordi Pons, Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez
You are welcome to contact Eduardo Fonseca should you have any questions, at email@example.com.
Freesound Dataset 50k (or FSD50K for short) is an open dataset of human-labeled sound events containing 51,197 Freesound clips unequally distributed in 200 classes drawn from the AudioSet Ontology . FSD50K has been created at the Music Technology Group of Universitat Pompeu Fabra.
What follows is a brief summary of FSD50K's most important characteristics. Please have a look at our paper (especially Section 4) to extend the basic information provided here with relevant details for its usage, as well as discussion, limitations, applications and more.
vocabulary.csv(see Files section below).
Note: All classes in FSD50K are represented in AudioSet, except
Human group actions,
Respiratory sounds, and
Domestic sounds, home sounds.
All audio clips in FSD50K are released under Creative Commons (CC) licenses. Each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. For attribution purposes and to facilitate attribution of these files to third parties, we include a mapping from the audio clips to their corresponding licenses. The licenses are specified in the files
eval_clips_info_FSD50K.json. These licenses are CC0, CC-BY, CC-BY-NC and CC Sampling+.
In addition, FSD50K as a whole is the result of a curation process and it has an additional license: FSD50K is released under CC-BY. This license is specified in the
LICENSE-DATASET file downloaded with the
FSD50K.doc zip file.
Usage of FSD50K for commercial purposes:
If you'd like to use FSD50K for commercial purposes, please contact Eduardo Fonseca and Frederic Font at firstname.lastname@example.org and email@example.com.
FSD50K can be downloaded as a series of zip files with the following directory structure:
root │ └───FSD50K.dev_audio/ Audio clips in the dev set │ └───FSD50K.eval_audio/ Audio clips in the eval set │ └───FSD50K.ground_truth/ Files for FSD50K's ground truth │ │ │ └─── dev.csv Ground truth for the dev set │ │ │ └─── eval.csv Ground truth for the eval set │ │ │ └─── vocabulary.csv List of 200 sound classes in FSD50K │ └───FSD50K.metadata/ Files for additional metadata │ │ │ └─── class_info_FSD50K.json Metadata about the sound classes │ │ │ └─── dev_clips_info_FSD50K.json Metadata about the dev clips │ │ │ └─── eval_clips_info_FSD50K.json Metadata about the eval clips │ │ │ └─── pp_pnp_ratings_FSD50K.json PP/PNP ratings │ │ │ └─── collection/ Files for the *sound collection* format │ └───FSD50K.doc/ │ └───README.md The dataset description file that you are reading │ └───LICENSE-DATASET License of the FSD50K dataset as an entity
Each row (i.e. audio clip) of
dev.csv contains the following information:
fname: the file name without the
.wavextension, e.g., the fname
64760corresponds to the file
64760.wavin disk. This number is the Freesound id. We always use Freesound ids as filenames.
labels: the class labels (i.e., the ground truth). Note these class labels are smeared, i.e., the labels have been propagated in the upwards direction to the root of the ontology. More details about the label smearing process can be found in Appendix D of our paper.
mids: the Freebase identifiers corresponding to the class labels, as defined in the AudioSet Ontology specification
split: whether the clip belongs to train or val (see paper for details on the proposed split)
eval.csv follow the same format, except that there is no
Note: We use a slightly different format than AudioSet for the naming of class labels in order to avoid potential problems with spaces, commas, etc. Example: we use
Accelerating_and_revving_and_vroom instead of the original
Accelerating, revving, vroom. You can go back to the original AudioSet naming using the information provided in
vocabulary.csv (class label and mid for the 200 classes of FSD50K) and the AudioSet Ontology specification.
Files with additional metadata (FSD50K.metadata/)
To allow a variety of analysis and approaches with FSD50K, we provide the following metadata:
class_info_FSD50K.json: python dictionary where each entry corresponds to one sound class and contains:
FAQs utilized during the annotation of the class,
examples (representative audio clips), and
verification_examples (audio clips presented to raters during annotation as a quality control mechanism). Audio clips are described by the Freesound id. Note: It may be that some of these examples are not included in the FSD50K release.
dev_clips_info_FSD50K.json: python dictionary where each entry corresponds to one dev clip and contains: title, description, tags, clip license, and the uploader name. All these metadata are provided by the uploader.
eval_clips_info_FSD50K.json: same as before, but with eval clips.
pp_pnp_ratings.json: python dictionary where each entry corresponds to one clip in the dataset and contains the PP/PNP ratings for the labels associated with the clip. More specifically, these ratings are gathered for the labels validated in the validation task (Sec. 3 of paper). This file includes 59,485 labels for the 51,197 clips in FSD50K. Out of these labels:
Ratings' legend: PP=1; PNP=0.5; U=0; NP=-1.
Note: The PP/PNP ratings have been provided in the validation task. Subsequently, a subset of these clips corresponding to the eval set was exhaustively labeled in the refinement task, hence receiving additional labels in many cases. For these eval clips, you might want to check their labels in
eval.csv in order to have more info about their audio content (see Sec. 3 for details).
collection/: This folder contains metadata for what we call the sound collection format. This format consists of the raw annotations gathered, featuring all generated class labels without any restriction.
We provide the collection format to make available some annotations that do not appear in the FSD50K ground truth release. This typically happens in the case of classes for which we gathered human-provided annotations, but that were discarded in the FSD50K release due to data scarcity (more specifically, they were merged with their parents). In other words, the main purpose of the
collection format is to make available annotations for tiny classes. The format of these files in analogous to that of the files in
FSD50K.ground_truth/. A couple of examples show the differences between collection and ground truth formats:
In the first example, raters provided the label
Owl. However, due to data scarcity,
Owl labels were merged into their parent
Bird. Then, labels
Wild_Animal,Animal were added via label propagation (smearing). The second example shows one of the most extreme cases, where raters provided the labels
Electric_toothbrush,Toothbrush, which both had few data. Hence, they were merged into Toothbrush's parent, which unfortunately is
Domestic_sounds_and_home_sounds (a rather vague class containing a variety of children sound classes).
Note: Labels in the collection format are not smeared.
Note: While in FSD50K's ground truth the vocabulary encompasses 200 classes (common for dev and eval), since the collection format is composed of raw annotations, the vocabulary here is much larger (over 350 classes), and it is slightly different in dev and eval.
For further questions, please contact firstname.lastname@example.org, or join the freesound-annotator Google Group.
FSD50K.doc/ are compressed into one zip file each. However, due to their large size, the folders
FSD50K.eval_audio/ are split into several files. Specifically,
FSD50K.dev_audio/ is split into six files (note the last file is not
FSD50K.dev_audio.z01 FSD50K.dev_audio.z02 FSD50K.dev_audio.z03 FSD50K.dev_audio.z04 FSD50K.dev_audio.z05 FSD50K.dev_audio.zip
In this case, you first have to download all the files. Once downloaded, we merge all the files into one zip file called e.g.
unsplit.zip in your local machine.
zip -s 0 FSD50K.dev_audio.zip --out unsplit.zip
Finally, this merged file is unzipped.
Similar guidelines must be followed for the
FSD50K.eval_audio/ folder (only two zip files in this case).
Several baseline systems for FSD50K are available at https://github.com/edufonseca/FSD50K_baseline. The experiments are described in Sec 5 of our paper.
REFERENCES AND LINKS
 Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter. "Audio set: An ontology and human-labeled dataset for audio events." In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, 2017. [PDF]
 Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra. "Freesound Datasets: A Platform for the Creation of Open Audio Datasets." In Proceedings of the International Conference on Music Information Retrieval, 2017. [PDF]
Companion site for FSD50K: https://annotator.freesound.org/fsd/release/FSD50K/
Freesound Annotator: https://annotator.freesound.org/
Eduardo Fonseca's personal website: http://www.eduardofonseca.net/
More datasets collected by us: http://www.eduardofonseca.net/datasets/
The authors would like to thank everyone who contributed to FSD50K with annotations, and especially Mercedes Collado, Ceren Can, Rachit Gupta, Javier Arredondo, Gary Avendano and Sara Fernandez for their commitment and perseverance. The authors would also like to thank Daniel P.W. Ellis and Manoj Plakal from Google Research for valuable discussions. This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 AudioCommons, and two Google Faculty Research Awards 2017 and 2018, and the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).