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.

SoundSpaces 2.0 是一个用于 3D 环境的实时几何声音渲染平台，用于生成高度逼真的声学效果以支持多种音频和视觉研究任务，并可用于普及性研究。

SoundSpaces 2.0: 一个视听学习仿真平台

SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning

We present enhancements to a speech-to-speech translation pipeline in order
to perform automatic dubbing. Our architecture features neural machine
translation generating output of preferred length, prosodic alignment of the
translation with the original speech segments, neural text-to-speech with fine
tuning of the duration of each utterance, and, finally, audio rendering to
enriches text-to-speech output with background noise and reverberation
extracted from the original audio. We report on a subjective evaluation of
automatic dubbing of excerpts of TED Talks from English into Italian, which
measures the perceived naturalness of automatic dubbing and the relative
importance of each proposed enhancement.

本文提出了一种基于神经网络的语音 - 语音翻译系统，实现了自动配音。实验结果表明自动配音的自然度得到了显著提升，包括多个技术创新点，如神经机器翻译、音频渲染、语音对位、语音合成等。