Method and electronic apparatus for detecting tampering audio, and storage medium

A method for detecting audio tampering, the method comprising:

• acquiring a signal;
• performing a wavelet transform of a first preset order on the signal so as to obtain a first set of low-frequency coefficients and a first set of high-frequency coefficients corresponding to the signal, wherein the number of coefficients in the first set of low-frequency coefficients and the number of coefficients in the first set of high-frequency coefficients are equal to the order of the first preset order;
• setting each of the first low-frequency coefficients to zero, and setting the first high-frequency coefficients having an order less than a second preset order to zero, and performing an inverse wavelet transform on the first set of high-frequency coefficients having an order greater than or equal to the second preset order so as to obtain a first high-frequency component signal corresponding to the signal;
• calculating a first Mel cepstrum feature of the first high-frequency component signal in units of frame;
• concatenating the first Mel cepstrum features of a current signal frame and the first Mel cepstrum features of a preset number of preceding signal frames that arrived before the current signal frame so as to obtain a first concatenating feature, wherein the first Mel cepstrum features of the preset number of the preceding signal frames are obtained in a same manner as the first Mel cepstrum features of the current signal frame;
• and
• performing a detection of audio tampering on the first concatenating feature by means of a deep learning model,
• wherein the deep learning model has been trained, has learned and stored a correspondence between the first concatenating feature of the signal frames and whether the signal frames have been subjected to audio tempering;
• wherein calculating a first Mel cepstrum feature of the first high-frequency component signal in units of frame comprises:
• performing a fast Fourier transform on the first high-frequency component signal so as to obtain a transformation result;
• calculating a second Mel cepstrum feature of the transformation result in units of frame;
• and
• performing a discrete cosine transform on the second Mel cepstrum feature so as to obtain the first Mel cepstrum feature;
• wherein calculating a second Mel cepstrum feature of the transformation result in units of frame comprises calculating a second Mel cepstrum feature of the transformation result according to the following formula:
• X
• Mel
• (
• i
• )
• =
• log
• ⁡
• (
• ∑
• f
• =
• 1
• F
• H
• i
• (
• f
• )
• ⁢
• ❘
• "\[LeftBracketingBar]"
• X
• ⁡
• (
• f
• )
• ❘
• "\[RightBracketingBar]"
• 2
• )
• ,
• 1
• ≤
• i
• ≤
• a
• ,
• wherein, X(f) is the transformation result;
• |X(f)| is a norm operation of X(f);
• F is the number of frequency bands;
• f is a serial number of the frequency bands;
• i is a serial number of a Mel filter;
• Hi(f) is a value of an i-th Mel filter in an f-th frequency band;
• a is a positive integer greater than 1;
• and XMel(i) is the second Mel cepstrum feature corresponding to the i-th Mel filter.