I am trying to replicate this article but its corresponding github repo is written quite badly. In the article, an NN is trained on manually corrupted audio signals. Unfortunately, the researchers did not add the audio files nor a clean code that show how they have corrupted their audio files. In the paper they write:
..for the noisy test set, the 100 utterances were corrupted with four unseen noise types (engine, white, street, and baby cry), at six SNR levels (-6 dB, 0 dB, 6 dB, 12 dB, 18 dB, and 24 dB); for the enhanced set, the utterances in the noisy set were enhanced by the enhancement model above.
Now to the question - is there a python (R/MATLAB libraries are fine as well) that takes as an input the signal, the type of desired noise and the SNR and returns a corrupted signal? If not, where do I get an engine or a crying baby noise types?
Thanks!