thickstun2018invariances
by jthickstun
Experiments for Invariances and Data Augmentation for Supervised Music Transcription
AI summary
Music transcription experiments
This collection of Jupyter notebooks presents experiments on invariances and data augmentation for supervised music transcription.
- stars
- 31
- forks
- 4
- watching
- 6
Similar projects
Found by comparing what the projects do, not just their names.
Music transcription tool
Transcribes music into lead sheets using machine learning and audio analysis
Music Transcriber
Automates music transcription of various audio elements, including instruments, vocals, and drums.
Music Analysis Tutorial
An interactive tutorial on using Deep Learning algorithms to analyze and retrieve music information
Music tagger
Research code for music auto-tagging using deep learning and feature extraction
Music theory library
A JavaScript music theory library with tools for transposition, interval calculation, chord parsing and identification
Dependency manager
This tool helps manage and optimize dependencies in Jupyter Notebooks.
Music Classifier
A repository providing tools and pre-trained models for music classification and regression tasks using transfer learning.
Statistical learning textbook implementation
Notes and code implementation of statistical learning concepts from a textbook
Synthesizer library
A sequencing WebAudio synthesizer library supporting ABC notation
Jupyter Notebooks
A collection of Jupyter Notebook examples showcasing the use of the Earth Engine Python API
Music info retrieval tutorial
A tutorial on using deep learning techniques for music information retrieval tasks
Computer Science Textbook
Jupyter notebooks and accompanying resources for teaching computer science fundamentals
Music Classifier
Automatically categorizes music genres using deep learning algorithms
Jupyter Lab
A Dockerized environment for quantitative research and analysis using Jupyter Notebook
Music classifier
A repository containing code and pre-trained models for music classification using Convolutional Recurrent Neural Networks.
