Audio Machine Learning
Table of Contents
Background
This page contains all the resources that I used for my masters thesis and my interest in machine learning and audio. This page is inspired by Meinard Müller, Valerio Valardo, Brian McFee, Michael Scott Asato Cuthbert, Herman Dong who have generously shared their course materials and created resources to make audio machine learning more accessible.
Foundations
Programming
Signal Processing
- Audio Signal Processing for Machine Learning
- Digital Signals Theory
- George Tzanetakis - A Music and Sound Exposition of the DFT
Sequential Modeling
Machine Learning
- Deep Learning for Audio with Python
- PCPT: A Preparation Course in PyTorch. Through the Principles of Signal Processing - Book Website(Free Download)
- PyTorch for Audio + Music Processing
- Audio Data Augmentation
- Melody Generation with RNN-LSTM
Music Processing
Speech Processing
Generative AI
AI Music
Potpurri
Datasets
NSYNTH- MagentaMAESTRO- MagentaUrbanSound8K- MARL, kaggleMNIST- PyTorch DatasetsGTZAN- kaggle
Conferences
Audio
Music
Speech
Machine Learning
Be aware that these conferences have a lot of problems with submissions and reviewing. They sometimes have audio and music workshops but really depends on what the organizers decide every year.