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Echo

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...

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Related Experiment Video

Updated: Aug 22, 2025

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Deep Non-Line-of-Sight Imaging Using Echolocation.

Seungwoo Jang¹, Ui-Hyeon Shin¹, Kwangsu Kim²

¹Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces acoustic non-line-of-sight (NLOS) imaging, using deep learning to reconstruct hidden scenes faster. The novel method effectively visualizes objects by analyzing modified echoes, overcoming limitations of traditional optical and acoustic techniques.

Keywords:

acoustic sensing deep learning depth estimation non-line-of-sight

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Area of Science:

Acoustics
Computer Vision
Machine Learning

Background:

Non-line-of-sight (NLOS) imaging visualizes hidden scenes using diffused signals, typically with optical equipment like lasers.
Existing acoustic NLOS methods, inspired by seismic imaging, are computationally intensive, noise-prone, and require long data acquisition times.
Optical NLOS imaging is common due to laser efficiency but has limitations.

Purpose of the Study:

To develop a novel non-line-of-sight (NLOS) imaging technique using acoustic equipment.
To accelerate data acquisition and improve reconstruction accuracy in acoustic NLOS imaging.
To address challenges posed by signal interference in acoustic NLOS imaging.

Main Methods:

Proposed acoustic NLOS imaging inspired by echolocation, contrasting with traditional optical methods.
Reduced scan time by collecting acoustic echoes simultaneously instead of sequentially.
Developed end-to-end deep learning models with specialized encoder, generator, and discriminator architectures to process complex echo data.

Main Results:

Successfully reconstructed the outline of hidden objects using the proposed acoustic NLOS imaging system.
Demonstrated the efficacy of deep learning models in overcoming echo interference.
Achieved reduced scan times compared to conventional acoustic NLOS imaging methods.

Conclusions:

Acoustic NLOS imaging combined with deep learning offers a viable alternative to optical methods for visualizing hidden scenes.
The developed deep learning framework effectively handles complex acoustic data for improved imaging.
This approach shows promise for faster and more robust NLOS imaging applications.