Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
- 2021Listen, Read, and Identify: Multimodal Singing Language Identification of Musiccited by 2
- 2022A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognitioncited by 0
- 2024SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languagescited by 0
- 2025ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integrationcited by 0
- 2025ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration12 May 2025
- 2024SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages14 Mar 2024
- 2022A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition2 Nov 2022
- 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classification17 Jun 2021
- 2021Listen, Read, and Identify: Multimodal Singing Language Identification of Music2 Mar 2021