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How does the smartphone noise reduction microphone perform noise reduction based on AI voice recognition

Author:admin   AddTime:2021-02-21

Microphone noise reduction chip supplier smartphone noise reduction microphones mainly deal with environmental noise in two ways: realize the noise reduction function through multiple MICs. This method needs to be equipped with noise reduction MIC, which requires high user gestures. When using the hands-free mode, if the noise reduction MIC just presses the user's face, or in the headset mode, the noise reduction effect cannot be obtained. The other is to achieve noise reduction by building a software algorithm for scene noise reduction in the terminal device. This places high requirements on the computing power of the terminal. On the one hand, the number of samples for environmental noise is limited. On the other hand, the more noise samples, the longer the terminal's judgment time will be, which will affect the user experience.



The microphone noise reduction chip supplier found that these two traditional noise reduction methods have various defects, and the specific noise reduction effect is not satisfactory. However, through AI to identify the scene, the microphone noise reduction chip supplier smartphone noise reduction microphone is better The way is to use an independent noise reduction chip to reduce noise. The advantage of this method is that only a single MIC is required to ensure a clear voice in the call mode. In addition, this method does not need to search and match the noise samples.

The core of the microphone noise reduction chip supplier is the AI processor designed for voice noise reduction. The original sound processing output is realized through the DNN network noise reduction solution. Each layer of the network has 1028 nodes. In addition, the computing power of the AI processor can reach 1012 ops/W, which can realize calculation processing for specially compressed sound signals.

The reason why microphone noise reduction chip suppliers do not need to search and match noise samples. Mainly because the AI processor has performed machine learning on more than 400 million voice data samples, and can quickly find more suitable models and parameters for each audio segment through the DNN network. It can realize intelligent identification of various noise scenes including restaurants, buses, subways, railway stations, wind noise, and music noise.

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