Detecção de edições em áudios baseada na análise tempo-frequência e em redes neurais convolucionais

Abstract

Tampering detection is one of the most important topics in forensic analysis of digital audio. Splicing corresponds to the insertion of a segment of signal from a different audio into the original audio record and is included among the most common categories of tampering. Convolutional neural networks (CNNs) have demonstrated effectiveness in various audio processing tasks, which motivates research into different methods of obtaining input data. The spectrogram is a useful representation for visualizing the temporal evolution of the frequency spectrum of an audio, with different signal processing techniques available for its generation. In this study, the development of an automatic splicing detection model in digital audio using CNNs was carried out. The audio spectrogram, computed using different techniques such as Short-Time Fourier Transform (STFT) on a linear scale, STFT on a mel scale, and Constant Q Transform (CQT), was directly provided to the network as input data. A comparative study was conducted to evaluate the impact of the choice of time-frequency representation on the model’s performance in correctly classifying the original and edited audios.