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

arXiv: https://arxiv.org/abs/1706.05781 · HTML: https://mlsanity.com/paper/1706.05781/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2017.

## Built on by (most cited first, 6 of 6)

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- [Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models](https://mlsanity.com/paper/2112.05588/) (2021)
- [Listen, Read, and Identify: Multimodal Singing Language Identification of Music](https://mlsanity.com/paper/2103.01893/) (2021)
- [A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition](https://mlsanity.com/paper/2211.01263/) (2022)
- [SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages](https://mlsanity.com/paper/2403.09753/) (2024)
- [ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration](https://mlsanity.com/paper/2505.07709/) (2025)

## Abstract (arXiv)

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.