PUB - Publikationen an der Universität Bielefeld

This paper presents a method to use t-SNE for training low-dimensional timbre maps obtained from musical recordings in a way that similar sound spectra are represented at similar location within the map. To use such maps for generating novel sounds and timbre trajectories, we use Kernel Regression Mapping for the inverse transformation from map space to timbre space and Griffin-Lim algorithm for phase reconstruction. With this mix of methods, we achieve a way to both visually explore timbral patterns and compress control data. As an application for navigable timbre maps we introduce a novel method – related to and inspired from Wave Space Sonification (WSS) – for the auditory exploration of patterns in multivariate time-series data by using the semantic sound map as the connecting representation. We demonstrate our approach by sonifying ECG data relating to cardiac pathologies.