Crowdsourcing a Reverberation Descriptor Map
Crowdsourcing a Reverberation Descriptor Map
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众包混响描述符图
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Bryan Pardo
中科院分区：
文献类型：
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作者：
Prem Seetharaman;Bryan Pardo
音频生产是每一种涉及声音的媒体的核心，如电影、电视和音乐，并涉及将音频转换为一种供公众消费的状态。最常用的音频制作工具之一是混响器。目前的界面通常很复杂，很难理解。我们寻求通过让用户用描述性语言(例如“把鼓的声音调大一些。”)为了实现这一目标，系统必须能够辨别所述目标是否适合所选工具(例如，使用平移工具使小提琴变暖没有意义)。如果目标适合于工具，它必须知道什么行动会导致目标。此外，该工具不应该将词汇表强加给用户，而应该理解用户喜欢的词汇表。在这项工作中，我们描述了SocialReverb，这是一个将音频描述符的词汇表众包的项目，可以使用参数混响器将其映射到具体的行动上。我们在机械土耳其人上部署了SocialReverb，其中513个独立用户使用2861个唯一单词描述了256个混响实例。我们使用这些数据构建了一张概念图，显示哪些单词是流行的描述符，哪些单词一致地映射到特定的混响类型，以及哪些单词是同义词。这有望实现未来的界面，让用户使用自然语言来交流他们的生产需求。
Audio production is central to every kind of media that involves 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 complex and hard-to-understand. We seek to simplify these interfaces 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 vocabulary of audio descriptors that can be mapped onto concrete 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 consistently 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.