CNN-Based Electronic Camouflage Audio Restoration Mechanism Zhengyu Shi
| Autor: | Lixian Zheng, Xiao Zhang, Yongquan Wang, Zhengyu Shi, Libo Wu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Speech recognition Audio restoration Process (computing) 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network GeneralLiterature_MISCELLANEOUS Convolution Transformation (function) Sampling (signal processing) 020204 information systems Camouflage 0202 electrical engineering electronic engineering information engineering Companding 0105 earth and related environmental sciences |
| Zdroj: | ICSAI |
| Popis: | This paper proposes a restoration mechanism based on convolution neural network (CNN) for electronic camouflage audio. Since there are certain change rules in the process of converting original audio into electronic camouflage audio and audio is short-time stationary, convolution and nonlinear mapping are performed on the historical sampling acoustic information and restoring factors of the electronic camouflage audio. After companding transformation, the reduction audio is outputted. In the experiment, the voiceprint features comparison, LPC analysis and human ear identity judgement are made between restoring audio and original audio. The results show the validity of the proposed mechanism. It is of great theoretical and practical significance to the restoration of electronic camouflage audio in judicial expertise. |
| Databáze: | OpenAIRE |
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