Reimplementation of Modeling Nonlinear Audio Effects with End-to-end Deep Neural Networks

Experimental implementation of Modeling Nonlinear Audio Effects with End-to-end Deep Neural Networks. Original work by Marco Martínez

Notice

This repository is incomplete. It was a reimplementation before the release of Marco's journal paper and accompanying code for all of the end-to-end analog audio effect models: https://github.com/mchijmma/DL-AFx

TODO

• Smooth Adaptive Activation Function (SAAF) - with an adaptive tanh on the backend this model produces artefacts.

Requirements

Python 3.6.6 CUDNN keras 2.1.6 tensorflow-gpu 1.12.0

bash config

For a consisent environment configuration, add the following to your ~/.bashrc

module load python/3.6.6

export PATH=${PATH}:/usr/local/cuda-10.0/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-10.0/lib64

module load cuda/10.0-cudnn7.4.2

export CUDA_VISIBLE_DEVICES=0

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