  Reusing recorded sounds (sampling) is a key component in Electronic Music
Production (EMP), which has been present since its early days and is at the
core of genres like hip-hop or jungle. Commercial and non-commercial services
allow users to obtain collections of sounds (sample packs) to reuse in their
compositions. Automatic classification of one-shot instrumental sounds allows
automatically categorising the sounds contained in these collections, allowing
easier navigation and better characterisation. Automatic instrument
classification has mostly targeted the classification of unprocessed isolated
instrumental sounds or detecting predominant instruments in mixed music tracks.
For this classification to be useful in audio databases for EMP, it has to be
robust to the audio effects applied to unprocessed sounds. In this paper we
evaluate how a state of the art model trained with a large dataset of one-shot
instrumental sounds performs when classifying instruments processed with audio
effects. In order to evaluate the robustness of the model, we use data
augmentation with audio effects and evaluate how each effect influences the
classification accuracy.
