  A recurrent issue in deep learning is the scarcity of data, in particular
precisely annotated data. Few publicly available databases are correctly
annotated and generating correct labels is very time consuming. The present
article investigates into data augmentation strategies for Neural Networks
training, particularly for tasks related to drum transcription. These tasks
need very precise annotations. This article investigates state-of-the-art sound
transformation algorithms for remixing noise and sinusoidal parts, remixing
attacks, transposing with and without time compensation and compares them to
basic regularization methods such as using dropout and additive Gaussian noise.
And it shows how a drum transcription algorithm based on CNN benefits from the
proposed data augmentation strategy.
