# Freesound General-Purpose Audio Tagging Challenge — Data 来源：https://www.kaggle.com/competitions/freesound-audio-tagging/data 采集时间：2026-10-24T15:15:40Z **UPDATE**: If you are interested in the **FSDKaggle2018** dataset used for this competition, we recommend to download it from **Zenodo**. The Zenodo version contains **all the files** of dataset, without the addition of a set of *padding* clips in the test set that are not used for evaluation. In this way, you won't download these **unnecessary** files and the download will be faster. **Please download FSDKaggle2018 at** **https://zenodo.org/record/2552860#.XFD05fwo-V4** ---- ---- For this competition, the objective is to predict the audio classification label for the audio files found in the `audio_test` folder. ## File descriptions * **train.csv** - ground truth labels and other information relating to the training audio files (see Data Fields below) * **audio_train.zip** - a folder containing the audio (.wav) training files * **audio_test.zip** - a folder containing the audio (.wav) test files * **sample_submission.csv** - a sample submission file in the correct format; contains the list of files found in the `audio_test` folder ### Update: Post Competition Files * **train_post_competition.csv** - includes `freedsound_id` and `license` information * **test_post_competition.csv** - includes label ground truth, LB Split (`usage`), `freedsound_id` and `license` information ## Citation If you participated to this task, or you use the FSDKaggle2018 dataset or baseline code, please cite our DCASE 2018 paper: > Eduardo Fonseca, Manoj Plakal, Frederic Font, Daniel P. W. Ellis, > Xavier Favory, Jordi Pons, Xavier Serra. *General-purpose Tagging of > Freesound Audio with AudioSet Labels: Task Description, Dataset, and > Baseline*. In Proceedings of DCASE2018 Workshop, 2018. URL: > https://arxiv.org/abs/1807.09902 You can also consider citing our ISMIR 2017 paper, where we introduce the Freesound Annotator that was used to build the FSDKaggle2018 dataset: > Eduardo Fonseca, Jordi Pons, Xavier Favory, Frederic Font, Dmitry > Bogdanov, Andrés Ferraro, Sergio Oramas, Alastair Porter, and Xavier > Serra. *Freesound datasets: a platform for the creation of open audio > datasets*. In Proceedings of the 18th International Society for Music > Information Retrieval Conference (ISMIR 2017), pp 486-493. Suzhou, > China, 2017. ## Data fields Each row of the `train.csv` file contains the following information: - `fname`: the file name - `label`: the audio classification label (ground truth) - `manually_verified`: Boolean (1 or 0) flag to indicate whether or not that annotation has been manually verified; see below for additional background ## About this dataset Freesound Dataset Kaggle 2018 (or **FSDKaggle2018** for short) is an audio dataset containing 18,873 audio files annotated with labels from Google's [AudioSet](https://research.google.com/audioset/) Ontology [1]. All audio samples in this dataset are gathered from [Freesound](https://freesound.org) [2] and are provided here as uncompressed PCM 16 bit, 44.1 kHz, mono audio files. Note that because Freesound content is collaboratively contributed, recording quality and techniques can vary widely. All sounds in Freesound are released under Creative Commons (CC) licenses. In particular, all Freesound sounds included in FSDKaggle2018 are released under either [CC-BY](https://creativecommons.org/licenses/by/3.0/) or [CC0](https://creativecommons.org/publicdomain/zero/1.0/). For attribution purposes and to facilitate attribution of these files to third parties, this dataset includes a relation of audio files and their corresponding license. The file with the **relation of licenses will be published after the competition is concluded** in order to ensure data anonymity during the competition. The ground truth data provided in this dataset has been obtained after a data labeling process which is described in the *Data labeling process* section below. FSDKaggle2018 sounds are unequally distributed in the following **41 categories** of the AudioSet Ontology: ``` "Acoustic_guitar", "Applause", "Bark", "Bass_drum", "Burping_or_eructation", "Bus", "Cello", "Chime", "Clarinet", "Computer_keyboard", "Cough", "Cowbell", "Double_bass", "Drawer_open_or_close", "Electric_piano", "Fart", "Finger_snapping", "Fireworks", "Flute", "Glockenspiel", "Gong", "Gunshot_or_gunfire", "Harmonica", "Hi-hat", "Keys_jangling", "Knock", "Laughter", "Meow", "Microwave_oven", "Oboe", "Saxophone", "Scissors", "Shatter", "Snare_drum", "Squeak", "Tambourine", "Tearing", "Telephone", "Trumpet", "Violin_or_fiddle", "Writing" ``` Here are some other relevant characteristics of FSDKaggle2018: * The dataset is split into a **train set** and a **test set**. * The **train set** is meant to be for system development and includes **~9.5k samples unequally distributed among 41 categories**. The minimum number of audio samples per category in the train set is 94, and the maximum 300. The duration of the audio samples ranges from 300ms to 30s due to the diversity of the sound categories and the preferences of Freesound users when recording sounds. * Out of the ~9.5k samples from the train set, **~3.7k have manually-verified ground truth annotations** and **~5.8k have non-verified annotations**. The non-verified annotations of the train set have a quality estimate of at least 65-70% in each category. Checkout the *Data labeling process* section below for more information about this aspect. * Non-verified annotations in the train set are properly flagged in `train.csv` so that participants can opt to use this information during the development of their systems. * The **test set** is composed of **~1.6k samples with manually-verified annotations** and with a similar category distribution than that of the train set. The test set is complemented with ~7.8k *padding* sounds which are not used for scoring the systems. * All audio samples in this dataset have a **single label** (i.e. are only annotated with one label). Checkout the *Data labeling process* section below for more information about this aspect. ## Data labeling process The data labeling process started from a manual mapping between Freesound tags and AudioSet Ontology categories (or *labels*), which was carried out by researchers at the [Music Technology Group](http://mtg.upf.edu/) (Universitat Pompeu Fabra, Barcelona). Using this mapping, a number of Freesound audio samples were **automatically annotated** with labels from the AudioSet Ontology. These annotations can be understood as weak labels since they express the presence of a sound category in an audio sample. Then, a **data validation process** was carried out in which a number of participants did listen to the annotated sounds and manually assessed the presence/absence of an automatically assigned sound category, according to the AudioSet category description. Audio samples in FSDKaggle2018 are only annotated with a single ground truth label (see `train.csv`). A total of **3,710 annotations** included in the train set of FSDKaggle2018 are annotations that have been **manually validated** as present and predominant (some with inter-annotator agreement but not all of them). This means that in most cases there is no additional acoustic material other than the labeled category. In few cases there may be some additional sound events, but these additional events won't belong to any of the 41 categories of FSDKaggle2018. The rest of the annotations have **not** been manually validated and therefore some of them could be inaccurate. Nonetheless, we have **estimated** that at least 65-70% of the non-verified annotations per category **in the train set** are indeed correct. It can happen that some of these non-verified audio samples present several sound sources even though only one label is provided as ground truth. This additional sources are typically out of the set of the 41 categories, but in a few cases they could be within. More details about the data labeling process can be found in [3]. ## References [1] 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 dartaset for audio events." Proceedings of the Acoustics, Speech and Signal Pro- cessing International Conference, 2017. [2] Frederic Font, Gerard Roma, and Xavier Serra. "Freesound technical demo." Proceedings of the 21st ACM international conference on Multimedia, 2013. [https://freesound.org](https://freesound.org) [3] 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." Proceedings of the International Conference on Music Information Retrieval, 2017. [PDF here](https://repositori.upf.edu/bitstream/handle/10230/33299/fonseca_ismir17_freesound.pdf)