We introduce SoundSpaces 2.0, a platform for on-the-fly geometry-based audio rendering for 3D environments. Given a 3D mesh of a real-world environment, SoundSpaces can generate highly realistic acoustics for arbitrary sounds captured from arbitrary microphone locations. Together with existing 3D visual assets, it supports an array of audio-visual research tasks, such as audio-visual navigation, mapping, source localization and separation, and acoustic matching. Compared to existing resources, SoundSpaces 2.0 has the advantages of allowing continuous spatial sampling, generalization to novel environments, and configurable microphone and material properties. To our knowledge, this is the first geometry-based acoustic simulation that offers high fidelity and realism while also being fast enough to use for embodied learning. We showcase the simulator's properties and benchmark its performance against real-world audio measurements. In addition, we demonstrate two downstream tasks---embodied navigation and far-field automatic speech recognition---and highlight sim2real performance for the latter. SoundSpaces 2.0 is publicly available to facilitate wider research for perceptual systems that can both see and hear.

Many factors such as the tendency of individuals to develop relationships based on mutual acquaintances, proximity, common interests, or combinations thereof, are known to contribute toward evolution of social networks. In this paper, we analyze an evolving online social aggregator FriendFeed, which collates content generated by participating individuals on a variety of Web 2.0 services and allows easy dissemination of the aggregated content to other participants of the aggregator. Analyzing data collected between September 2008 and May 2009, we find that although preferential attachment captures the evolution of the network, its influence varies significantly based on how long ago a user joined the service. In particular, preferential attachment does not appear to apply to new entrants of the FriendFeed service. Analysis suggests that proximity bias plays an important role in link formation. We study the influence of common foci and find that individuals have a greater affinity toward those with similar interests.