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A time-domain nearfield beamformer with spherical harmonic decomposition.
Fei Ma1, Thushara D Abhayapala1, Prasanga N Samarasinghe1
1College of Engineering and Computer Science, The Australian National University, Canberra, Australian Capital Territory 0200, Australia.
The Journal of the Acoustical Society of America
|September 25, 2023
Summary
This study introduces a novel time-domain nearfield beamformer using spherical harmonic decomposition. This method enhances real-time acoustic applications by reducing latency and improving noise suppression for virtual reality and drones.
Area of Science:
- Acoustics
- Signal Processing
- Array Signal Processing
Background:
- Nearfield beamforming is crucial for localized sound source analysis.
- Existing methods often face challenges with latency and far-field noise suppression.
- Spherical harmonic decomposition offers a robust framework for spatial audio processing.
Purpose of the Study:
- To propose a novel time-domain nearfield beamformer utilizing spherical harmonic decomposition.
- To enhance the implementability and adaptability of beamformers for diverse array structures.
- To improve real-time performance by minimizing latency and maximizing far-field noise suppression.
Main Methods:
- The beamformer design is segmented into sound field measurement and coefficient design stages.
- Spherical harmonic decomposition is employed for spatial analysis.
- A time-domain approach is implemented to reduce signal processing latency.
Main Results:
- The proposed two-stage design simplifies implementation across various array configurations.
- Separating beamformer coefficients allows for tailored beamformer characteristics.
- Time-domain processing significantly reduces the input-to-response latency.
- Nearfield focusing demonstrably improves far-field noise suppression capabilities.
Conclusions:
- The developed time-domain nearfield beamformer offers a flexible and efficient solution.
- Its performance is validated through simulations and experimental results.
- The beamformer shows significant potential for advanced acoustic applications like virtual reality and drones.
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