Audio Mosaicing with Simulation-based Inference

Mosaics and collages have been an integral part of art for decades. Particularly important in contemporary media art is the audio mosaic, in which an artist manually combines several audio sources in order to construct one single coherent sound, combining elements from disparate sources. Here we propose an algorithm to automatically create audio mosaics using the simulation-based inference paradigm. Our algorithm takes as input an audio file that one wishes to approximate, and a list of audio files one can use for approximation, finding a posterior distribution from which one can sample reconstructions of the original audio file, using the sources in an interpretable and disentangled manner. We validate our approach by creating an audio mosaic which reconstructs the sound of a traditional Korean funeral using 100 K-pop songs rearranged and overlapped.