Abstract
The way people interact with machines through flexible acoustic sensors is revolutionizing the way we live. However, developing a human-machine interaction acoustic sensor that simultaneously offers low cost, high stability, high fidelity, and high sensitivity remains a significant challenge. In this study, a sensor based on a sound-driven triboelectric nanogenerator was proposed. A poly(vinylidene fluoride) (PVDF)/graphene oxide (GO) composite nanofiber film was obtained as the dielectric layer through electrospinning, and copper-nickel alloy conductive fabric was used as the electrode. An imitation embroidery shed structure was designed in the shape of a ring to secure the upper and lower electrodes and the dielectric layer as a whole. Due to the porosity of the electrode, the large contact area of the dielectric layer, and the high stability of the imitation embroidery shed structure, the sensor achieves a sensitivity of 4.76 V·Pa-1 and a frequency response range of 20-2000 Hz. A multilayer attention convolutional network (MLACN) was designed for speech recognition. The designed speech recognition system achieved a 99.5% accuracy rate in recognizing common word pronunciations. The integration of sound-driven triboelectric nanogenerator-based flexible acoustic sensors with deep learning techniques holds great promise in the field of human-machine interaction.
Published Version
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