  We measure the effect of small amounts of systematic and random label noise
caused by slightly misaligned ground truth labels in a fine grained audio
signal labeling task. The task we choose to demonstrate these effects on is
also known as framewise polyphonic transcription or note quantized multi-f0
estimation, and transforms a monaural audio signal into a sequence of note
indicator labels. It will be shown that even slight misalignments have clearly
apparent effects, demonstrating a great sensitivity of convolutional neural
networks to label noise. The implications are clear: when using convolutional
neural networks for fine grained audio signal labeling tasks, great care has to
be taken to ensure that the annotations have precise timing, and are free from
systematic or random error as much as possible - even small misalignments will
have a noticeable impact.
