Comparative Audio Analysis With Wavenet, MFCCs, UMAP, t-SNE and PCA

This post is on a project exploring an audio dataset in two dimensions. The results are hosted in a small web application on my university's servers -- have a play with it! Run the mouse over the purple dots to hear the sounds that are associated with the two-dimensional position vector. Feel free to play with the features used (MFCCs or Wavenet latent variables) and the method of dimensionality reduction (UMAP, t-SNE or PCA.) UMAP and t-SNE will also have parameters such as step amount or perplexity that can be tweaked. So what do we mean by dimensionality? It is an important topic in machine learning and data science that describes the potential complexity of a dataset. A dataset will comprise a multitude of data points, each having a constant amount of features, or dimensions.