CFCC space gradient-based keyboard single-key keystroke content identification method

Abstract

The invention discloses a CFCC space gradient-based keyboard single-key keystroke content identification method and belongs to the technical field of non-contact identification. The method is dividedinto an initial identification stage and a gradient identification stage. In the initial identification stage, a CFCC value of a keystroke sound signal is extracted. Keystroke content is recognized through a BP neural network method, the Manhattan distance and CFCC difference between keys with low recognition accuracy and other keys are calculated through a gradient recognition stage, a CSG matrixis constructed, a new training set and a new test set are constructed, the BP neural network is retrained, and a final classification result is obtained. The method can better alleviate the interference of the environment and the influence caused by the diversity of measurement equipment, is more robust than an original CFCC, enables the features of a sound signal to be differentiated in space, and is more stable in time.

Description

technical field [0001] The invention relates to the technical field of non-contact recognition, in particular to a CFCC space gradient-based method for recognizing keystroke content of a single key on a keyboard. Background technique [0002] As an important input device in daily life, the keyboard plays an important role in personal work safety and other aspects. Researchers try to study the behavioral characteristics of people typing on the keyboard and the changes in the surrounding electromagnetic wave signals, so as to identify the content of the user typing on the keyboard, or identify its application based on the keyboard input, and use these applications to promote context-aware services. [0003] Most of the current research programs are based on the analysis of the frequency spectrum of the sound signal. By analyzing the sound signal when the user hits the keyboard, the content entered by the user can be effectively identified. In 2004, Asonov et al. used sound si...

AI Technical Summary

Problems solved by technology

However, due to the unstable CSI characteristics of wireless signals and the existence of multipath effects when signals propagate in space, the acquisition, analysis and application of signals are extremely difficult

[0004] To sum up, the acoustic keystroke recognition method based on neural network is greatly affected by the noise in the actual environment, resulting in a small signal-to-noise ratio, so the accuracy is not very high

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