MSc AI Project on generative deep networks and neural style transfer for audio

We prepared a short script called server_script.py in the learn_decay folder which hopefully contains a detailed enough description on how to train a model and generate a test sequence.

Here you can listen to our results or read our project report.

Week 2 - 7/6/2016

[Week 2 - 10/6/2016](https://github.com/Fr-d-rik/generative_audio/blob/master/docs/Presentation 10-6-2016.pdf)

Final presentation

Introduction to RNN/LSTM

Oxford video lecture about RNN/LSTM Nando de Freitas

Keras for Sequence to Sequence Learning

An Empirical Exploration of Recurrent Network Architectures

Neat explanation of different LSTM variants with some good visualization

When and How to use TimeDistributedDense

Learning to generate text and audio

GRUV is a Python project for algorithmic music generation

fiala notes 1- Deep Learning and Sound ~ 01 Introduction

fiala notes 2- Deep Learning and Sound ~ 02: Generating Audio

Kaparthy's Stanford course CS231 on CNN

video - Simple(?) Step by Step

python - How to plot frequency spectrum?

Paper comparing different decay measures

Yaafe - package for audio feature extraction

Link to instrument files for first experiment Klick auf der Seite auf "Musical Instrument Samples/Post 2012/Woodwinds"

List of frequency ranges of many classical instruments

Stephan Mallat - Understanding Deep Convolutional Networks

JOINT TIME-FREQUENCY SCATTERING FOR AUDIO CLASSIFICATION

Junyoung Chung, Kyle Kastner, A Recurrent Latent Variable Model for Sequential Data

Variational Recurrent Auto-Encoder from Joost and Otto (UvA AI)

Justin Bayer & Christian Osendorfer, EARNING STOCHASTIC RECURRENT NETWORKS

Markup cheatsheet

Python & audio

Digital Signal Processing