  In the human ear, the basilar membrane plays a central role in sound
recognition. When excited by sound, this membrane responds with a
frequency-dependent displacement pattern that is detected and identified by the
auditory hair cells combined with the human neural system. Inspired by this
structure, we designed and fabricated an artificial membrane that produces a
spatial displacement pattern in response to an audible signal, which we used to
train a convolutional neural network (CNN). When trained with single frequency
tones, this system can unambiguously distinguish tones closely spaced in
frequency. When instead trained to recognize spoken vowels, this system
outperforms existing methods for phoneme recognition, including the discrete
Fourier transform (DFT), zoom FFT and chirp z-transform, especially when tested
in short time windows. This sound recognition scheme therefore promises
significant benefits in fast and accurate sound identification compared to
existing methods.

Audio and Speech Processing;Machine Learning;Sound;Biological Physics