  We propose a novel training scheme using self-label correction and data
augmentation methods designed to deal with noisy labels and improve real-world
accuracy on a polyphonic audio content detection task. The augmentation method
reduces label noise by mixing multiple audio clips and joining their labels,
while being compatible with multiple active labels. We additionally show that
performance can be improved by a self-label correction method using the same
pretrained model. Finally, we show that it is feasible to use a strong
zero-shot model such as CLAP to generate labels for unlabeled data and improve
the results using the proposed training and label enhancement methods. The
resulting model performs similar to CLAP while being an efficient mobile device
friendly architecture and can be quickly adapted to unlabeled sound classes.
