Physics-inspired Neuroacoustic Computing Based on Tunable Nonlinear Multiple-scattering

Understand

Waves, such as light and sound, inherently bounce and mix due to multiple scattering induced by the complex material objects that surround us.

• This scattering process severely scrambles the information carried by waves, challenging conventional communication systems, sensing paradigms, and wave-based computing schemes.
• Here, we show that instead of being a hindrance, multiple scattering can be beneficial to enable and enhance analog nonlinear information mapping, allowing for the direct physical implementation of computational paradigms such as reservoir computing and extreme learning machines.
• We propose a physics-inspired version of such computational architectures for speech and vowel recognition that operate directly in the native domain of the input signal, namely on real-sounds, without any digital pre-processing or encoding conversion and backpropagation training computation.