A Blind Detection Algorithm for The Faked High-Resolution Audio
With the advent of the High-definition age, consumers pay more and more attention to music’s quality, which the most important aspect is the resolution of the audio. Meanwhile, resolution is largely determined by the sampling rate. In order to tell real high sampling rate audio from resampled high sampling rate audio, this thesis proposes a blind detection algorithm for the faked high-resolution audio. The algorithm has 3 steps. First, a pre-processing step, including the process of taking the difference, taking the absolute value, taking the logarithm and taking Fourier transform, to a set of audio which we know if it has been resampled. Secondly, the output data of the first step is divided into several sub-bands, each band take the average as an eigenvalue. Thirdly, the output data of the second step is divided into two groups, training data and testing data, then import them to the SVM algorithm, which can train a model. The model can tell if an audio clip has been resampled. Experiments show that this algorithm is effective to tell if an audio clip has been resampled for different music style as well as different original sampling rate. Moreover, it can reach 98% correct rate when the audio clip is only 100ms.
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