Liu Hai, Liu Song, Shkel Anton A, Kim Eun Sok
Electrical Engineering Department, University of Southern California, Los Angeles, CA 90089 USA.
Electrical Engineering Department, University of Southern California, Los Angeles, CA 90089 USA. He is now with Facebook, Menlo Park, CA 94025 USA.
J Microelectromech Syst. 2020 Oct;29(5):839-845. doi: 10.1109/jmems.2020.3011938. Epub 2020 Aug 4.
This paper presents active noise cancelation (ANC) based on MEMS resonant microphone array (RMA) which offers very high sensitivities (and thus very low noise floors) near resonance frequencies and also provides filtering in acoustic domain. The ANC is targeted to actively cancel out any sound between 5 - 9 kHz (above the speech range of 300 - 3,400 Hz). The ANC works best around the resonance frequencies of the resonant microphones where the sensitivities are high. The ANC has been implemented with analog inverter, digital phase compensator, digital adaptive filter, and deep learning, and shown to perform better with a digital adaptive filter for both RMA-based and flat-band-microphone-based ANC. At the same time, when the sound intensity over 5 - 9 kHz is low, RMA-based ANC with adaptive filter works the best among different approaches tested. Automatic speech recognition under different noises (of different intensity levels) has been tested with ANC. In all the tested cases, word error rate improves with ANC.
本文介绍了基于微机电系统（MEMS）谐振麦克风阵列（RMA）的有源噪声消除（ANC）技术，该技术在谐振频率附近具有非常高的灵敏度（从而具有非常低的本底噪声），并且还能在声学领域提供滤波功能。该有源噪声消除技术旨在主动消除5至9千赫兹之间的任何声音（高于300至3400赫兹的语音范围）。有源噪声消除技术在谐振麦克风的谐振频率附近效果最佳，因为此时灵敏度较高。有源噪声消除技术已通过模拟反相器、数字相位补偿器、数字自适应滤波器和深度学习实现，并且对于基于RMA的有源噪声消除和基于平面麦克风的有源噪声消除，使用数字自适应滤波器时表现更佳。同时，当5至9千赫兹的声音强度较低时，基于RMA的带有自适应滤波器的有源噪声消除技术在所有测试方法中效果最佳。已经使用有源噪声消除技术测试了在不同噪声（不同强度水平）下的自动语音识别。在所有测试案例中，有源噪声消除技术都能降低单词错误率。
