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Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

Keunwoo Choi, Deokjin Joo, Juho Kim
Centre for Digital Music, Queen Mary University of London, London, UK
arXiv:1706.05781 [cs.SD], (19 Jun 2017)

@article{choi2017kapre,

   title={Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras},

   author={Choi, Keunwoo and Joo, Deokjin and Kim, Juho},

   year={2017},

   month={jun},

   archivePrefix={"arXiv"},

   primaryClass={cs.SD}

}

We introduce Kapre, Keras layers for audio and music signal preprocessing. Music research using deep neural networks requires a heavy and tedious preprocessing stage, for which audio processing parameters are often ignored in parameter optimisation. To solve this problem, Kapre implements time-frequency conversions, normalisation, and data augmentation as Keras layers. We report simple benchmark results, showing real-time on-GPU preprocessing adds a reasonable amount of computation.
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