A wave-confining metasphere beamforming acoustic sensor for superior human-machine voice interaction

Area of Science:

• Acoustic Metamaterials
• Sensor Technology
• Human-Machine Interaction

Background:

• Effective voice-based human-machine interaction requires highly sensitive, source-tracking acoustic sensing.
• Omnidirectionally tracking sound sources with hypersensitive rates and low noise in compact sensors presents a significant challenge.

Purpose of the Study:

• To present a novel unibody acoustic metamaterial spherical shell sensor for enhanced acoustic sensing.
• To demonstrate the sensor's capability for omnidirectional sound source perception and spatial filtering.

Main Methods:

• Development of a unibody acoustic metamaterial spherical shell with defected piezoelectric cavities, termed the metasphere beamforming acoustic sensor (MBAS).
• Evaluation of the MBAS's wave-confining capability, self-noise, sensitivity, and signal-to-noise ratio across phonetic frequencies (0-1500 Hz).
• Testing of an MBAS-based auditory system for audio cloning, source localization, and speech recognition in noisy environments.

Main Results:

• The MBAS exhibits excellent wave-confining properties and low self-noise.
• Achieved an outstanding intrinsic signal-to-noise ratio of 72 dB and ultrahigh sensitivity (137 mVpp/Pa).
• Demonstrated high-performance audio cloning, source localization, and speech recognition in noisy conditions without signal enhancement.

Conclusions:

• The metasphere beamforming acoustic sensor (MBAS) offers a breakthrough in compact, highly sensitive, and omnidirectional acoustic sensing.
• The MBAS technology shows significant promise for improving voice interaction systems, particularly in challenging noisy environments.