Skip to content

An experiment aimed to compare a range of ML-based music classifiers

Notifications You must be signed in to change notification settings

pmhalvor/music-genre-classifier

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

27 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

music-genre-classifier

An experiment aimed to compare a range of ML-based music classifiers

We test three different types of machine learning classifiers (traditional, simple neural networks, and pre-trained models) on the GTZAN Dataset - Music Genre Classification.

Classifier Types

We want to measure performance between complex models and simpler ones. The experiment aims to ultimately show how much compute resources are needed for good classifications.

Traditional

We train and briefly tune 3 traditional machine learning models: random forest, support vector machines, and k-nearest neighbors. Then measure the performance based on their macro F-1 scores. We also review category misclassification rate per category using a confusion matrix.

Neural networks

We then train three simple neural networks: feed-forward (mlp), cnn, rnn. This is mainly to establish a lower limit for performance of neural architectures. Minimal fine tuning should be done here to save resources and maintain the baseline score.

Pretrained models

There exists many open-source audio/music classifiers. We will select one publicly available through HuggingFace. When choosing a model here, it is important to have a reproducible preprocessing step. Once this preprocessing is set, then we can re-run the previous experiments, but with the same format of data as used here, for a fair comparison.

About

An experiment aimed to compare a range of ML-based music classifiers

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published