Mobile equipment source identification method and system based on multimode fusion depth features

Abstract

The invention belongs to the technical field of voice evidence obtaining. The invention discloses mobile device source identification method and system based on multimode fusion depth features. The method comprises the following steps: firstly, extracting MFCCs and GSV features of test data, correspondingly segmenting the features into multiple paths, then respectively training CNNs and performingfusion to obtain fused depth features, then determining the fused depth features by using a trained depth residual error network, and finally carrying out joint decision on the determination resultsof short samples of each path by adopting a voting method. According to the method, when the GMM-UBM model is trained, the data is screened according to the characteristics of phonemes and tones of the voice data, and a small amount of representative data is selected, so that the representation generalization of the model is ensured, the data calculation amount is reduced, and the modeling efficiency is improved; according to the method, the deep neural network is used for supervised training to extract the deep features, redundant and interference information in the feature data is eliminated, the feature data is simplified, the characterization of the data is improved, the dimensionality of the data is reduced, and the calculation amount is simplified.

Description

technical field [0001] The invention belongs to the technical field of voice forensics, and in particular relates to a mobile device source identification method and system based on multi-mode fusion depth features. Background technique [0002] Currently, the closest prior art: [0003] With the rapid development of digital media technology, various electronic products such as computers, digital cameras, mobile phones, printers, scanners, etc. have gradually become indispensable equipment in people's daily life, resulting in a large number of media files. At the same time, all kinds of professional digital media editing software are gradually becoming more convenient under the appeal of people. While these editing software bring convenience and joy to people's life, they also introduce many challenging problems. Some lawbreakers secretly record and forge a large amount of voice data through various recording equipment and editing software. A series of problems caused by t...

AI Technical Summary

Problems solved by technology

[0007] (1) The characteristic of the traditional mobile device source identification method is poor in further mining and improving the efficiency; and the traditional judgment model is relatively intuitive, and cannot fully represent the mobile device through feature information; the traditional test judgment method is Based on a single decision, the recognition accuracy is low

[0008] (2) Most of the previous methods directly use the original feature data to build the algorithm model, because the original feature data has a lot of redundancy and interference information, so it increases the amount of calculation when building the algorithm model, and also makes the final The algorithm model is not accurate enough

[0009] (3) Most of the current methods use a single feature data to model the characteristics of the device source

In terms of justice, voice data is becoming more and more important as evidence, but some forged and tampered voice data conceal the truth, thus bringing a lot of trouble to voice recognition

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