Sriram Subramanian

For more than a century, segmented displays, such as the seven‐segment display, have been a popular and cost‐effective option. They can display various commonly used characters by controlling a few custom‐shaped binary light emitters. Herein, the acoustic equivalent of segmented displays, which uses heterogeneous sound modulators to generate a limited set of acoustic holographic images, is introduced. Designing segmented acoustic displays is more challenging than optical ones due to the complex relationship between emitted sound and generated holographic images. To address this challenge, a design methodology based on unsupervised learning techniques is proposed. The approach balances the cost of acoustic displays and the quality of the images they generate, resulting in segmented displays that outperform existing general‐purpose ones when generating a finite set of acoustic images. Using simulations and physical fabrication of metamaterial‐based acoustic displays, it is proven that the approach can create segmented acoustic displays that produce high‐quality images at a lower cost. Additionally, the methodology is applied to multifrequency acoustic displays and its scalability is assessed as the number of images increases. The cost reduction through segmentation presented herein is expected to democratize sound manipulators for acoustic displays and other applications like acoustic levitation and noise cancellation.