Lossless or Lossy. lol tells whether a given wav has gone through lossy encoding
in its lifetime.
lol needs examples of pure lossless audios of your use case (the use case
shouldn’t matter much but this has to be validated) and a list of potential
audio transforms. Then it trains a model to predict whether a new set of audios
is lossless (1) or not (0).
lol
Usage:
lol train --audio-dir=<audio-dir> --transforms-file=<transforms-file> --output-model=<output-model>
lol --audio-dir=<audio-dir> --model=<model> --output-csv=<output-csv>
Options:
--transforms-file=<transforms-file> Plain text file with lines mapping to ffmpeg lossy transforms.Sample transforms file is present in ./resources/ directory.
Flipped mel filters give the following test results on a wav dataset:
precision recall f1-score support
0 1.00 1.00 1.00 112
1 1.00 1.00 1.00 102
accuracy 1.00 214
macro avg 1.00 1.00 1.00 214
weighted avg 1.00 1.00 1.00 214
As compared to regular filters:
precision recall f1-score support
0 0.96 0.91 0.94 112
1 0.91 0.96 0.93 102
accuracy 0.93 214
macro avg 0.93 0.94 0.93 214
weighted avg 0.94 0.93 0.93 214
Of course you might get more mileage by complicating the model, but this looks like a neat feature trick for the problem.