Audio tampering recognition method based on improved neural network

Abstract

The invention discloses an audio tampering recognition algorithm based on an improved neural network, which pools spectrograms of any size into a CNNs structure represented by a spectrogram of a fixed length and an LSTM structure with an attention mechanism, and the Mel spectrogram of the signal And frame-level features are introduced into the speech tampering recognition algorithm, which integrates the spectrum and timing information of the audio signal; by adding an improved pooling layer to the CNNs structure, CNNs can input spectrograms of any size to solve the problem of unfixed audio length; increase The attention mechanism mines the weight ratio of high-level features, and finally obtains high-quality audio features; and uses the data fusion theory to carry out decision-making fusion algorithms; improves the recognition rate of audio tampering recognition and the robustness of the model. The invention can effectively identify whether the audio has been tampered with or not, and overcomes the problem of low recognition rate of traditional audio tampering.

Description

technical field [0001] The invention belongs to the field of audio tampering, and in particular relates to an audio tampering recognition method based on an improved neural network. Background technique [0002] The increasing maturity of digital audio editing technology has destroyed the authenticity and integrity of digital audio. When falsified audio is used as evidence in court, it will have a great impact on the judgment of the case. Therefore, judging whether the audio has been tampered with or not is an urgent problem to be solved by the relevant judicial departments. [0003] In 2005, Grigoras.C found that there was a power grid frequency component in the recording signal powered by mains power, and extracted the frequency characteristics of the power grid in the audio to be tested to match and compare with the data in the power grid frequency characteristic database of the power supply department. With a high degree of similarity, it is proposed for the first time...

AI Technical Summary

Problems solved by technology

Firstly, there is insufficient research on the characteristics of audio tampering recognition; secondly, the audio tampering recognition model, the existing audio tampering models are all traditional signal processing models, and machine learning and deep learning are rarely used for analysis

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