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Crowdsourcing a reverberation descriptor map
| Content Provider | CiteSeerX |
|---|---|
| Author | Seetharaman, Prem Pardo, Bryan |
| Description | Audio production is central to every kind of media that in-volves sound, such as film, television, and music and involves transforming audio into a state ready for consumption by the public. One of the most commonly-used audio production tools is the reverberator. Current interfaces are often com-plex and hard-to-understand. We seek to simplify these in-terfaces by letting users communicate their audio production objective with descriptive language (e.g. “Make the drums sound bigger.”). To achieve this goal, a system must be able to tell whether the stated goal is appropriate for the selected tool (e.g. making the violin warmer using a panning tool does not make sense). If the goal is appropriate for the tool, it must know what actions lead to the goal. Further, the tool should not impose a vocabulary on users, but rather understand the vocabulary users prefer. In this work, we describe SocialReverb, a project to crowdsource a vocab-ulary of audio descriptors that can be mapped onto con-crete actions using a parametric reverberator. We deployed SocialReverb, on Mechanical Turk, where 513 unique users described 256 instances of reverberation using 2861 unique words. We used this data to build a concept map showing which words are popular descriptors, which ones map con-sistently to specific reverberation types, and which ones are synonyms. This promises to enable future interfaces that let the user communicate their production needs using natural language. |
| File Format | |
| Language | English |
| Publisher Institution | in Proceedings of the ACM International Conference on Multimedia, Series Crowdsourcing a Reverberation Descriptor Map, pp. 587--596, ACM, 2014 Published |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
