Thesis work. Research on audio clustering and classification possibilities, focusing on the applicability of some dimensionality reduction methods.
The procedure consists of multiple steps:
- Feature extraction: conversion of samples to a time-frequency representation. Methods: STFT, log scaled STFT, mel scale, MFCC. Various frequency bin numbers are tested.
- Reshaping these spectrograms in a way that it is possible to perform dimensionality reduction on them.
- Applying dimensionality reduction. Project to 2D in case of clustering, and higher dimensions in case of classification. Methods: PCA, t-SNE, Isomap, SOM (Self Organizing Map).
- Classification or Clustering
There's also a GUI implemented with PyQt5, that makes it possible to test and tune the methods described.
Some inspirational papers and articles are uploaded in the 'papers' folder.