  With increasing amounts of music being digitally transferred from production
to distribution, automatic means of determining media quality are needed.
Protection mechanisms in digital audio processing tools have not eliminated the
need of production entities located downstream the distribution chain to assess
audio quality and detect defects inserted further upstream. Such analysis often
relies on the received audio and scarce meta-data alone. Deliberate use of
artefacts such as clicks in popular music as well as more recent defects
stemming from corruption in modern audio encodings call for data-centric and
context sensitive solutions for detection. We present a convolutional network
architecture following end-to-end encoder decoder configuration to develop
detectors for two exemplary audio defects. A click detector is trained and
compared to a traditional signal processing method, with a discussion on
context sensitivity. Additional post-processing is used for data augmentation
and workflow simulation. The ability of our models to capture variance is
explored in a detector for artefacts from decompression of corrupted MP3
compressed audio. For both tasks we describe the synthetic generation of
artefacts for controlled detector training and evaluation. We evaluate our
detectors on the large open-source Free Music Archive (FMA) and genre-specific
datasets.
