Download Audio Visualization via Delay Embedding and Subspace Learning We describe a sequence of methods for producing videos from audio signals. Our visualizations capture perceptual features like harmonicity and brightness: they produce stable images from periodic sounds and slowly-evolving images from inharmonic ones; they associate jagged shapes to brighter sounds and rounded shapes to darker ones. We interpret our methods as adaptive FIR filterbanks and show how, for larger values of the complexity parameters, we can perform accurate frequency detection without the Fourier transform. Attached to the paper is a code repository containing the Jupyter notebook used to generate the images and videos cited. We also provide code for a realtime C++ implementation of the simplest visualization method. We discuss the mathematical theory of our methods in the two appendices.
Download Unsupervised Text-to-Sound Mapping via Embedding Space Alignment This work focuses on developing an artistic tool that performs an
unsupervised mapping between text and sound, converting an input text string into a series of sounds from a given sound corpus.
With the use of a pre-trained sound embedding model and a separate, pre-trained text embedding model, the goal is to find a mapping between the two feature spaces. Our approach is unsupervised which allows any sound corpus to be used with the system.
The tool performs the task of text-to-sound retrieval, creating a
soundfile in which each word in the text input is mapped to a single sound in the corpus, and the resulting sounds are concatenated
to play sequentially. We experiment with three different mapping
methods, and perform quantitative and qualitative evaluations on
the outputs. Our results demonstrate the potential of unsupervised
methods for creative applications in text-to-sound mapping.