Nonlinear dimensionality reduction approaches applied to music and textural sounds

Recently, various dimensionality reduction approaches have been proposed as alternatives to PCA or LDA. These im-proved approaches do not rely on a linearity assumption, and are hence capable of discovering more complex embeddings within different regions of the data sets. Despite their success on artificial datasets, it is not straightforward to predict which technique is the most appropriate for a given real dataset. In this paper, we empirically evaluate recent techniques on two real audio use cases: musical instrument loops used in music production and sound effects used in sound editing. ISOMAP and t-SNE are being compared to PCA in a visualization prob-lem, where we end up with a two-dimensional view. Various evaluation measures are used: classification performance, as well as trustworthiness/continuity assessing the preservation of neighborhoods. Although PCA and ISOMAP can yield good continuity performance even locally (samples in the original space remain close-by in the low-dimensional one), they fail to preserve the structure of the data well enough to ensure that distinct subgroups remain separate in the visual-ization. We show that t-SNE presents the best performance, and can even be beneficial as a pre-processing stage for im-proving classification when the amount of labeled data is low. Index Terms — Dimensionality reduction, multimedia in-formation retrieval, audio and music analysis