Sound non-reciprocity based on synthetic magnetism.

Synthetic magnetism has been recently realized using spatiotemporal modulation patterns, producing non-reciprocal steering of charge-neutral particles such as photons and phonons. Here, we design and experimentally demonstrate a non-reciprocal acoustic system composed of three compact cavities interlinked with both dynamic and static couplings, in which phase-correlated modulations induce a synthetic magnetic flux that breaks time-reversal symmetry. Within the rotating wave approximation, the transport properties of the system are controlled to efficiently realize large non-reciprocal acoustic transport. By optimizing the coupling strengths and modulation phases, we achieve frequency-preserved unidirectional transport with 45-dB isolation ratio and 0.85 forward transmission. Our results open to the realization of acoustic non-reciprocal technologies with high efficiency and large isolation, and offer a route towards Floquet topological insulators for sound.

Efficient and High-Purity Sound Frequency Conversion with a Passive Linear Metasurface.

Despite the significance for wave physics and potential applications, high-efficiency frequency conversion of low-frequency waves cannot be achieved with conventional nonlinearity-based mechanisms with poor mode purity, conversion efficiency, and real-time reconfigurability of the generated harmonic waves in both optics and acoustics. Rotational Doppler effect provides an intuitive paradigm to shifting the frequency in a linear system which, however, needs a spiral-phase change upon the wave propagation. Here a rotating passive linear vortex metasurface is numerically and experimentally presented with close-to-unity mode purity (>93%) and high conversion efficiency (>65%) in audible sound frequency as low as 3000 Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable the researchers to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. This strategy takes a step further toward the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection.

Meta-neural-network for real-time and passive deep-learning-based object recognition.

Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the "intelligence" to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.