III: Small: MicSynth: Enhancing and Reconstructing Sound Scenes from Crowdsourced Recordings

毫无疑问，我们生活在一个随时被多人大量记录的环境中。虽然我们有技术在视觉空间中组合联合收割机这样的信息（例如，与PhotoSynth），目前没有好的方法来组合来自同一事件的多个记录的联合收割机音频流。该项目通过开发频谱分解和基于地标的定位方法的新技术来填补这一空白，以支持对同一事件进行大量低级别音频记录并将其重新合成为一个高质量版本，消除每个单独录音的伪像和噪音，同时利用它们的优点。该项目旨在引入新的计算工具，以大规模联合收割机组合未经策划的录音。规模，并产生信息，没有单一的记录可以提供。结合所有可用的信息并产生客观的表示将能够有效地筛选来自大量记录事件的数据（例如，社会动荡、自然灾害、历史时刻），并关注所需信息。由此产生的工具将支持从历史事件中创建高质量的记录，否则可能无法通过使用人群的力量来很好地记录。该项目的网站（http：//www.cs.illinois.edu/crowparis/crowdmic）将提供查阅研究结果的机会，包括提供一项服务，将用户提交的记录、出版物和源代码合并起来，以促进这一领域的活动。研究成果也将被纳入音频和信号处理的社会和众包方面的课程开发。

There is no doubt that we live in an environment that is massively recorded by multiple people at any point in time. Although we have the technology to combine such information in the visual space (e.g., with PhotoSynth), there is currently no good way to combine audio streams from multiple recordings of the same event. This projects fills that gap by developing new techniques in spectral decompositions and landmark-based localization methods to support taking large amounts of low-level audio recordings of the same event and resynthesize them as one high-quality version, eliminating the artifacts and noise of each individual recording while taking advantage of their strong points.This project aims to introduce new computational tools to combine uncurated recordings at a large scale, and produce information that no single recording can provide. Combining all available information and producing objective representations will enable effective sifting through data from massively recorded events (e.g., social unrest, natural disasters, historical moments) and focus on the needed information. The resulting tools will support creation of high-quality recordings from historical events that might not otherwise be well documented, by using the power of the crowds. The project web site (http://www.cs.illinois.edu/~paris/crowdmic) will provide access to the research results, including a service that allows the consolidation from user-submitted recordings, publication and source code in order to to stimulate activity in this field. Research results will also be incorporated in the development of classes on social and crowdsourcing aspects of audio and signal processing.