This paper describes a hierarchical clustering of musical signals based on information derived from spectral and bispectral acoustic distortion measures. This clustering reveals the ultra metric structure that exists in the set of sounds, with a clear interpretation of the distances between the sounds as the statistical divergence between the sound models. Spectral, bispectral and combined clus...

Spectral clustering is one of the most widely used techniques for extracting the underlying global structure of a data set. Compressed sensing and matrix completion have emerged as prevailing methods for efficiently recovering sparse and partially observed signals respectively. We combine the distance preserving measurements of compressed sensing and matrix completion with the power of robust s...