Overview

OM-Pursuit is a compositional tool for corpus-based sound modelling.

Parametric sound representations have a tradition serving as conceptual models in composition contexts (see e.g. the French spectralist school). Today there are a number of software tools allowing composers to derive symbolic data from continuous sound phenomena, such as extracting frequency structures via sinusoidal partial tracking. Most of these tools, however, are based on the Fourier transform -which decomposes time-domain frames of a signal into frequency-domain (sinusoidal) components. This method can be less adequate to faithfully represent non-stationary sounds such as noise and transients, for example. Dictionary-based methods offer a different model, representing sound as a linear combination of individual atoms stored in a dictionary. Mostly used for sparse representation of signals for audio coding purposes (compression, transmission, etc), this atomic model offers interesting new possibilities for computer-aided composition applications, such as algorithmic transcription, orchestration, and sound synthesis.

Using sound samples as atoms for the dictionary we can design the 'timbral vocabulary' of an analysis/synthesis system. The dictionaries used to analyze a given audio signal can be built from arbitrary collections of sounds, such as instrumental samples, synthesized sounds, recordings, etc. OM-Pursuit uses an adapted matching pursuit algorithm (see pydbm by Graham Boyes), to iteratively approximate a given sound using a combination of the samples in the dictionary -in a sense comparable to photo-mosaicing techniques in the visual domain.

Some sound examples can be found on soundcloud