@inproceedings{7f5ac59259b540198d59145f3c7ea74b,
title = "SocialFX: Studying a crowdsourced folksonomy of audio effects terms",
abstract = "We present the analysis of crowdsourced studies into how a population of Amazon Mechanical Turk Workers describe three commonly used audio effiects: equalization, reverberation, and dynamic range compression. We find three categories of words used to describe audio: ones that are generally used across effiects, ones that tend towards a single effect, and ones that are exclusive to a single effect. We present select examples from these categories. We visualize and present an analysis of the shared descriptor space between audio effiects. Data on the strength of association between words and effiects is made available online for a set of 4297 words drawn from 1233 unique users for three effiects (equalization, reverberation, compression). This dataset is an important step towards implementing of an end-to-end language-based audio production system, in which a user describes a creative goal, as they would to a professional audio engineer, and the system picks which audio effect to apply, as well as the setting of the audio effect.",
keywords = "Audio engineering, Compression, Crowdsourcing, Effects processing, Equalization, Interfaces, Reverberation, Signal processing, Vocabulary",
author = "Taylor Zheng and Prem Seetharaman and Pardo, {Bryan A}",
note = "Funding Information: We would like to thank NSF Grants 1116384 and 1420971 for funding this work. Thanks to Alison Wahl for providing source audio for SocialFX.; 24th ACM Multimedia Conference, MM 2016 ; Conference date: 15-10-2016 Through 19-10-2016",
year = "2016",
month = oct,
day = "1",
doi = "10.1145/2964284.2967207",
language = "English (US)",
series = "MM 2016 - Proceedings of the 2016 ACM Multimedia Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "182--186",
booktitle = "MM 2016 - Proceedings of the 2016 ACM Multimedia Conference",
}