In this paper, we propose a method for detecting marks of lossy compression encoding, such as MP3 or AAC, from PCM audio. The
method is based on a convolutional neural network (CNN) applied
to audio spectrograms and trained with the output of various lossy
audio codecs and bitrates. Our method shows good performances on
a large database and robustness to codec type and resampling.
The core idea is that lossy compression leaves traces in the spectrogram of processed files, namely holes (areas of the Time-Frequency plane where values are put to zero) band frequency cuts, and clusters.
Using proper training data, most existing lossy compression algorithm are detected by our system with high accuracy.
![Performances](https://newsroom-deezer.com/wp-content/uploads/2023/09/spectro_artefacts.png)
This paper has been published in the proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017).