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 ...
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.
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