Dataset Information

Acoustic echoes reveal room shape.

ABSTRACT: Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room's response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? We show how to compute the shape of a convex polyhedral room from its response to a known sound, recorded by a few microphones. Geometric relationships between the arrival times of echoes enable us to "blindfoldedly" estimate the room geometry. This is achieved by exploiting the properties of Euclidean distance matrices. Furthermore, we show that under mild conditions, first-order echoes provide a unique description of convex polyhedral rooms. Our algorithm starts from the recorded impulse responses and proceeds by learning the correct assignment of echoes to walls. In contrast to earlier methods, the proposed algorithm reconstructs the full 3D geometry of the room from a single sound emission, and with an arbitrary geometry of the microphone array. As long as the microphones can hear the echoes, we can position them as we want. Besides answering a basic question about the inverse problem of room acoustics, our results find applications in areas such as architectural acoustics, indoor localization, virtual reality, and audio forensics.

SUBMITTER: Dokmanic I

PROVIDER: S-EPMC3725100 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Acoustic echoes reveal room shape.

Dokmanic Ivan I Parhizkar Reza R Walther Andreas A Lu Yue M YM Vetterli Martin M

Proceedings of the National Academy of Sciences of the United States of America 20130617 30

Imagine that you are blindfolded inside an unknown room. You snap your fingers and listen to the room's response. Can you hear the shape of the room? Some people can do it naturally, but can we design computer algorithms that hear rooms? We show how to compute the shape of a convex polyhedral room from its response to a known sound, recorded by a few microphones. Geometric relationships between the arrival times of echoes enable us to "blindfoldedly" estimate the room geometry. This is achieved ...[more]

PMID: 23776236

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Project description:Active echolocation allows blind individuals to explore their surroundings via self-generated sounds, similarly to dolphins and other echolocating animals. Echolocators emit sounds, such as finger snaps or mouth clicks, and parse the returning echoes for information about their surroundings, including the location, size, and material composition of objects. Because a crucial function of perceiving objects is to enable effective interaction with them, it is important to understand the degree to which three-dimensional shape information extracted from object echoes is useful in the context of other modalities such as haptics or vision. Here, we investigated the resolution of crossmodal transfer of object-level information between acoustic echoes and other senses. First, in a delayed match-to-sample task, blind expert echolocators and sighted control participants inspected common (everyday) and novel target objects using echolocation, then distinguished the target object from a distractor using only haptic information. For blind participants, discrimination accuracy was overall above chance and similar for both common and novel objects, whereas as a group, sighted participants performed above chance for the common, but not novel objects, suggesting that some coarse object information (a) is available to both expert blind and novice sighted echolocators, (b) transfers from auditory to haptic modalities, and (c) may be facilitated by prior object familiarity and/or material differences, particularly for novice echolocators. Next, to estimate an equivalent resolution in visual terms, we briefly presented blurred images of the novel stimuli to sighted participants (N = 22), who then performed the same haptic discrimination task. We found that visuo-haptic discrimination performance approximately matched echo-haptic discrimination for a Gaussian blur kernel σ of ~2.5°. In this way, by matching visual and echo-based contributions to object discrimination, we can estimate the quality of echoacoustic information that transfers to other sensory modalities, predict theoretical bounds on perception, and inform the design of assistive techniques and technology available for blind individuals.

Dataset Information

Acoustic echoes reveal room shape.

Publications

Acoustic echoes reveal room shape.

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