In‐Sensor Passive Speech Classification with Phononic Metamaterials

Abstract: Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always‐on computers. However, their use in machine learning applications has not been explored due to the complexity of their design process. Current phononic metamaterials are restricted to simple geometries (e.g., periodic and tapered) and hence do not possess sufficient expressivity to encode machine learning tasks. A non‐periodic phononic metamaterial, directly from data samples, that can distinguish between pairs of spoken words in the presence of a simple readout nonlinearity is designed and fabricated, hence demonstrating that phononic metamaterials are a viable avenue towards zero‐power smart devices.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: In‐Sensor Passive Speech Classification with Phononic Metamaterials ; day:09 ; month:01 ; year:2024 ; extent:8 ; Advanced functional materials ; (09.01.2024) (gesamt 8)

Contributors, Places and Time

• Creator: Dubček, Tena ; Moreno‐Garcia, Daniel ; Haag, Thomas ; Omidvar, Parisa ; Thomsen, Henrik R. ; Becker, Theodor S. ; Gebraad, Lars ; Bärlocher, Christoph ; Andersson, Fredrik ; Huber, Sebastian D. ; van Manen, Dirk‐Jan ; Villanueva, Luis Guillermo ; Robertsson, Johan O.A. ; Serra‐Garcia, Marc

Further information

• DOI: 10.1002/adfm.202311877
• URN: urn:nbn:de:101:1-2024011014060925375711
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:26 AM CEST