Holographic Sound Field Inspection System
for Embodied Intelligent Robots

An Edge-Side Acoustic Perception Solution Designed for Humanoid Robots

I. Industry Pain Points and Problem Definition

1.1 The Perception Blind Zone in Humanoid Robot Inspection

The deployment of humanoid robots for inspection in industrial scenarios such as power and oil & gas is accelerating, but their perception systems rely mainly on modalities such as visible-light vision and infrared thermal imaging. This combination has a structural deficiency—the absence of wide-area acoustic perception capability. It manifests in three categories of problems:

Problem One: The contradiction between a local field of view and global monitoring

Both vision and infrared are directional sensing modalities, with field-of-view angles typically not exceeding 90°. For a robot to cover the entire space, it must repeatedly rotate its pan-tilt or adjust its position—point-by-point scanning is inefficient, and transient signals generated during rotation are very easily missed.

Problem Two: Weak fault signals are hard to capture

The acoustic signals generated by early faults such as partial discharge and gas leakage (40–50 dB) are far lower than the background noise of the industrial environment (80–90 dB). Even when the robot happens to face the direction of the fault, the weak signal is completely submerged in the strong noise.

Problem Three: Conventional audio algorithms fail in dynamic environments

While a humanoid robot walks or turns its head, the geometric relationship between the sound source and the sensor array changes in real time. Conventional beamforming algorithms rely on Direction-of-Arrival (DOA) estimation, whose convergence delay is typically more than 200 ms—within which the robot has already changed its heading, causing the beam to point at a “ghost location,” with signal loss and loss of context.

These three problems together mean that today’s humanoid robots have weakened auditory capability in complex sound field environments.

1.2 Industry Status: Limitations of Existing Acoustic Inspection Solutions

Industrial acoustic inspection currently relies mainly on the following solutions:

None of the existing solutions can meet the requirement of “panoramic perception while moving” for humanoid robots. The industry lacks an acoustic perception system designed specifically for embodied intelligent agents.

II. Solution Overview

2.1 System Positioning

The Holographic Sound Field Inspection System is an edge-side acoustic perception solution designed specifically for humanoid robots. Centered on the holographic sound field inspection instrument as its core hardware, the system integrates a 3D spatial hearing algorithm stack and an edge AI inference engine, enabling the robot to perform real-time acquisition, reconstruction, localization, and diagnosis of the full-space sound field while in motion.

2.2 System Architecture

The system adopts a three-layer architecture of “Perception–Cognition–Decision,” fully deployed on the robot body itself:

Architectural principles:

III. Core Technologies

3.1 Spherical Array: Native Panoramic Sound Field Acquisition

Technical description:

The system uses 64 high-signal-to-noise-ratio (≥ 80 dB) microphones arranged in a spherical topology. The geometric symmetry of the spherical array gives it an isotropic acoustic response characteristic over 360° horizontal and 180° vertical—incident sound waves from any direction are received by the same number of microphones at the same sensitivity, achieving full-space coverage without mechanical rotation.

Differences from conventional solutions:

The sensitivity of a planar array decays as the incidence angle increases (the cosine effect), and its effective field of view is typically less than 90°; expanding coverage requires pan-tilt rotation, introducing mechanical delay and rotational inertia. A spherical array has no concept of a “primary viewing direction,” and sources anywhere in the full space are responded to equally.

Significance for robots:

When the robot reaches any inspection point, a single acquisition by the spherical array completes an all-direction sound field snapshot without adjusting heading or posture. Compared with “point-by-point scanning,” the improvement in time efficiency scales linearly with the expansion of the covered angle—to cover the same solid angle, a spherical array requires no rotation, whereas a planar array requires N rotations and waits (N = solid angle ÷ field-of-view angle).

3.2 3D Spatial Source Localization: Robust Localization in Dynamic Scenes

Problem definition:

Conventional beamforming algorithms rely on DOA estimation, whose convergence depends on a sufficiently long observation time window (typically 200–500 ms). Within this window, a humanoid robot may already have rotated—with the result that the beam points in the direction where the user or the source previously was (a “ghost” direction) and the signal is lost. This is an inherent defect of beamforming algorithms on mobile platforms and cannot be solved by optimizing the DOA estimator itself.

Technical route:

Holographic sound field inspection adopts an interferometric localization route that is independent of DOA estimation.

Basic principle: the path lengths along which a sound wave travels from the source to different microphones on the spherical array differ, producing frequency-dependent phase differences among the signals received by the various channels. This phase-difference pattern is uniquely determined by the spatial geometric relationship between the source and the array and is independent of the array’s orientation. The system performs a full-band joint solution of the multi-channel phase differences and directly outputs the three-dimensional (X, Y, Z) coordinates of the source.

Key technical details:

• The algorithm exploits the spatial acoustic fingerprint formed by direct sound and wall-reflected sound—for sound emitted from the same position, the arrival-time differences and amplitude ratios of the direct sound and the various orders of reflected sound form a unique pattern that is independent of the robot’s heading;

• The system does not rely on prior statistical characteristics of the source signal, making it applicable to inspection scenarios involving unknown sources;

• The localization output is continuous three-dimensional coordinates (not discrete angular grid points), usable directly for robotic arm guidance.

Significance for robots:

The source localization result is decoupled from the robot’s own heading—the robot can continuously output stable three-dimensional source coordinates while walking or turning its head, without the “ghost-chasing” problem.

3.3 Deep Noise Reduction and Multi-Source Blind Source Separation

Problem definition:

In industrial scenarios, target fault signals (PD ultrasound, gas-leak turbulence) are far weaker in intensity than background noise and overlap heavily with it in both the time and frequency domains. Conventional methods such as spectral subtraction and Wiener filtering degrade sharply under low signal-to-noise ratio conditions (SNR < 0 dB).

Technical route:

The system adopts a deep neural network-based multi-channel time–frequency masking scheme:

• Taking the multi-channel signals of the spherical array as input, it extracts joint spatial–spectral features in the time–frequency domain;

• The network outputs masking coefficients for each source in each time–frequency unit, achieving separation in the frequency domain;

• The separated target signals then undergo time-domain waveform reconstruction.

Measured performance:

• Multi-source separation signal-to-interference ratio (SIR) ≥ 12 dB;

Self-noise cancellation:

Components of the humanoid robot itself—motors, fans, joint drives—generate near-field noise that varies in real time with motion state. The system incorporates a built-in adaptive self-noise modeling module: using reference accelerometer and microphone signals mounted on the robot body, it estimates the self-noise transfer function in real time and cancels it from the array signals. The module’s runtime latency is less than 10 ms, ensuring that the robot is not “self-deafened” while walking.

3.4 Acoustic–Optical Multimodal Fusion

The spherical array carries a synchronized panoramic camera. The captured panoramic image, after processing by an AI stitching algorithm, is spatially aligned with the sound field reconstruction result at the pixel level—each pixel in the sound field contour map corresponds to a spatial point with known three-dimensional coordinates in the real scene. The final output is a panoramic visible-light image overlaid with the sound field contour map, presenting the spatial location, sound pressure level, and spectral characteristics of the fault point within the same frame.

IV. Application Scenarios and Value

4.1 Power Scenario: Partial Discharge Detection

4.2 Oil & Gas Scenario: Leak Detection and Localization

4.3 The Synergistic Gain of “Mobility + Panorama”

The core value of the system lies not only in “panorama” but in “panorama in motion”:

• The panoramic coverage of the spherical array maximizes the volume of information acquired at each stop, allowing a simpler inspection route—there is no need to repeatedly adjust the route in order to cover different directions;

• Historical inspection data accumulates by spatial coordinate to form a three-dimensional acoustic baseline for the area—in subsequent inspections, points that deviate from the baseline automatically trigger anomaly flags, realizing an evolution from “fault detection” to “baseline-deviation detection”;

• The three-dimensional coordinates of a fault point can be passed directly to the robotic arm control system, guiding the arm to approach the target for re-inspection or handling.

V. Technical Advantages and Comparison

VI. Conclusion

The Holographic Sound Field Inspection System solves a fundamental problem at the physical level: how a humanoid robot moving through three-dimensional space can perceive the full-space sound field stably, in real time, and with high precision.

The system’s technical route—spherical array, interferometric localization, DNN noise reduction, and self-noise cancellation—is designed entirely around this core problem, rather than simply transplanting a static-scenario acoustic solution onto a mobile platform. This “first-principles” design logic determines the system’s applicability and reliability on the specific carrier that is a humanoid robot.

We look forward to collaborating with partners in the field of embodied intelligence to jointly advance acoustic perception as a standard perception modality for humanoid robots.