A self-filtering liquid acoustic sensor for voice recognition

IF 33.7 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

Nature Electronics Pub Date : 2024-09-23 DOI:10.1038/s41928-024-01196-y

Xun Zhao, Yihao Zhou, Aaron Li, Jing Xu, Shreesh Karjagi, Edward Hahm, Lara Rulloda, Justin Li, John Hollister, Pirouz Kavehpour, Jun Chen

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Abstract

Wearable acoustic sensors can be used for voice recognition. However, the capabilities of such devices, which are typically based on solid materials, are often restricted by ambient noise, motion artefacts and low conformability to the skin. Here we report a liquid acoustic sensor for voice recognition. The approach is based on a three-dimensional oriented and ramified magnetic network structure of neodymium–iron–boron magnetic nanoparticles suspended in a carrier fluid, which behaves like a permanent magnet. The sensor can discriminate small pressures (0.9 Pa), has a high signal-to-noise ratio (69.1 dB) and provides self-filtering capabilities that can remove low-frequency biomechanical motion artefact (less than 30 Hz). We use the liquid acoustic sensor—together with a machine learning algorithm—to create a wearable voice recognition system that offers a recognition accuracy of 99% in a noisy environment. An acoustic sensor that is based on a network of magnetic nanoparticles suspended in a carrier fluid can be used—together with a machine learning algorithm—to create a wearable voice recognition system with an accuracy of 99% in a noisy environment.

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用于语音识别的自滤波液体声学传感器

可穿戴声学传感器可用于语音识别。然而，这类设备通常基于固体材料，其功能往往受到环境噪声、运动伪影和皮肤低顺应性的限制。在此，我们报告了一种用于语音识别的液体声学传感器。该方法基于悬浮在载液中的钕铁硼磁性纳米粒子的三维定向和柱状磁网络结构，其行为类似于永磁体。该传感器可分辨较小的压力（0.9 Pa），具有较高的信噪比（69.1 dB），并具有自滤波功能，可消除低频生物力学运动伪影（低于 30 Hz）。我们利用液体声学传感器和机器学习算法创建了一个可穿戴语音识别系统，该系统在嘈杂环境中的识别准确率高达 99%。

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来源期刊

Nature Electronics Engineering-Electrical and Electronic Engineering

CiteScore

47.50

自引率

2.30%

发文量

159

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