Sound Source Tracking and Formation using Normalized Cuts

The goal of computational auditory scene analysis (CASA) is to create computer systems that can take as input a mixture of sounds and form packages of acoustic evidence such that each package most likely has arisen from a single sound source. We formulate sound source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. It measures both the total dissimilarity between the different groups as well as the total similarity within groups. We describe how this formulation can be used with sinusoidal modeling, a common technique for sound analysis, manipulation and synthesis. Several examples showing the potential of this approach are provided.