  Previous research contributions on blind lossy compression identification
report near perfect performance metrics on their test set, across a variety of
codecs and bit rates. However, we show that such results can be deceptive and
may not accurately represent true ability of the system to tackle the task at
hand. In this article, we present an investigation into the robustness and
generalisation capability of a lossy audio identification model. Our
contributions are as follows. (1) We show the lack of robustness to codec
parameter variations of a model equivalent to prior art. In particular, when
naively training a lossy compression detection model on a dataset of music
recordings processed with a range of codecs and their lossless counterparts, we
obtain near perfect performance metrics on the held-out test set, but severely
degraded performance on lossy tracks produced with codec parameters not seen in
training. (2) We propose and show the effectiveness of an improved training
strategy to significantly increase the robustness and generalisation capability
of the model beyond codec configurations seen during training. Namely we apply
a random mask to the input spectrogram to encourage the model not to rely
solely on the training set's codec cutoff frequency.
