Freesound technical demo

By Frederic Font, Gerard Roma, Xavier Serra. 2013

403 citations recorded by the source corpus.

Freesound is an online collaborative sound database where people with diverse interests share recorded sound samples under Creative Commons licenses. It was started in 2005 and it is being maintained to support diverse research projects and as a service to the overall research and artistic community. In this demo we want to introduce Freesound to the multimedia community and show its potential as a research resource. We begin by describing some general aspects of Freesound, its architecture and functionalities, and then explain potential usages that this framework has for research applications.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

SEANet: A Multi-modal Speech Enhancement Network (2020, neutral): “We consider two scenarios: i) mixed speech , in which an utterance from a different speaker is mixed with the clean source; ii) mixed noise , in which we mix with samples taken at random from Freesound [27], to mimic a wide and diverse range of noise sources, with a unit mixing gain.”
FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “1) Freesound: Freesound8 is an online collaborative audio clip sharing site [53], counting with more than 10 million registered users, over 460,000 audio clips, and an average of 3,400 new clips added every month.”
Model-Agnostic Approaches To Handling Noisy Labels When Training Sound Event Classifiers (2019, methodology): “[24] E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, A. Ferraro, S. Oramas, A. Porter, and X. 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), Suzhou, China”
Hearing aid Research Data Set for Acoustic Environment Recognition (2020, neutral): “, Freesound [12]).”
Robust Low Rate Speech Coding Based on Cloned Networks and Wavenet (2020, methodology): “These were mixed with additive noise from Freesound [32] and a set of recordings captured in a variety of environments, including busy streets, cafes and ofﬁces.”
Freesound Datasets: A Platform for the Creation of Open Audio Datasets (2017, methodology): “In regards to the ﬁrst factor, we plan to leverage Freesound, an online collaborative audio sample sharing site that has been supporting diverse research and artistic purposes since 2005 [7].”
Audio tagging with noisy labels and minimal supervision (2019, neutral): “[21].”
Improving Sound Event Detection in Domestic Environments using Sound Separation (2020, background): “The foreground events are obtained from FSD50k [19, 20].”
Learning Sound Event Classifiers from Web Audio with Noisy Labels (2019, background): “More information about Freesound can be found in [15, 16].”
Sound Event Detection in Synthetic Domestic Environments (2020, positive): “Theforeground events are obtained from the Freesound Dataset (FSD) [14, 15].”
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment (2023, methodology): “…presented in Method Dataset MSR-VTT MSVD DiDeMo ActivityNet R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 R@1 R@5 R@10 Zero-shot Audio-Language retrieval We compare zero-shot text-to-audio retrieval performance on Clotho (Font et al., 2013) datasets and Audio-caps (Kim et al., 2019) dataset in Table 5.”
Audio Retrieval with Natural Language Queries (2021, methodology): “C LOTHO [9] (audio-centric) is a dataset of described sounds that was also introduced for the task of audio captioning, with sounds sourced from the Freesound platform [11].”
ESC: Dataset for Environmental Sound Classification (2015, methodology): “All datasets consist of sound clips constructed from recordings available publicly through the Freesound project [6].”
ImageBind One Embedding Space to Bind Them All (2023, background): “50 Acc 400 Clotho (Clotho) [17] Retrieval - Recall 1045 AudioCaps (AudioCaps) [37] Retrieval - Recall 796 VGGSound (VGGS) [8] Audio cls.”
COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations (2020, methodology): “Sounds were accessed from the Freesound API on the 7th of May, 2019.”
Clotho: an Audio Captioning Dataset (2019, methodology): “All audio samples are from Freesound platform [9], and are of duration from 15 to 30 seconds.”
Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis (2019, neutral): “The foreground events are obtained from the FSD [15, 16].”
SpeechLM: Enhanced Speech Pre-Training With Unpaired Textual Data (2022, methodology): “The test set is made by [50], which is a mixture of LibriSpeech test-clean set and a noise subset of FreeSound [51] at different levels of signal-to-noise ratio (SNR).”
WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research (2023, background): “FreeSound [66] is an online collaborative sound sharing site started in 2005.”