A room's acoustic properties are a product of the room's geometry, the
objects within the room, and their specific positions. A room's acoustic
properties can be characterized by its impulse response (RIR) between a source
and listener location, or roughly inferred from recordings of natural signals
present in the room. Variations in the positions of objects in a room can
effect measurable changes in the room's acoustic properties, as characterized
by the RIR. Existing datasets of RIRs either do not systematically vary
positions of objects in an environment, or they consist of only simulated RIRs.
We present SoundCam, the largest dataset of unique RIRs from in-the-wild rooms
publicly released to date. It includes 5,000 10-channel real-world measurements
of room impulse responses and 2,000 10-channel recordings of music in three
different rooms, including a controlled acoustic lab, an in-the-wild living
room, and a conference room, with different humans in positions throughout each
room. We show that these measurements can be used for interesting tasks, such
as detecting and identifying humans, and tracking their positions.

现有 RIR 数据集要么不系统地变化室内物体的位置，要么由模拟的 RIR 构成。我们提供了 SoundCam，这是迄今为止最大的野外房间独特 RIR 数据集，包括 5,000 个 10 通道真实世界房间冲激响应测量和 2,000 个 10 通道音乐录音，涵盖不同房间（实验室、客厅和会议室）内的人员在不同位置，这些测量可以用于有趣的任务，如检测和识别人员以及跟踪其位置。

SoundCam: 利用室内声学定位人类的数据集

SoundCam: A Dataset for Finding Humans Using Room Acoustics

Measuring the acoustic characteristics of a space is often done by capturing
its impulse response (IR), a representation of how a full-range stimulus sound
excites it. This work generates an IR from a single image, which can then be
applied to other signals using convolution, simulating the reverberant
characteristics of the space shown in the image. Recording these IRs is both
time-intensive and expensive, and often infeasible for inaccessible locations.
We use an end-to-end neural network architecture to generate plausible audio
impulse responses from single images of acoustic environments. We evaluate our
method both by comparisons to ground truth data and by human expert evaluation.
We demonstrate our approach by generating plausible impulse responses from
diverse settings and formats including well known places, musical halls, rooms
in paintings, images from animations and computer games, synthetic environments
generated from text, panoramic images, and video conference backgrounds.

通过使用神经网络从单幅图像生成音响脉冲响应，可以模拟图片所示的空间中的混响特性，为测量空间的声学特征提供了一个更加便捷的方法。