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.