Modeling natural sounds with modulation cascade processes.
Richard Turner and Maneesh Sahani
Advances in Neural Information Processing Systems
, Cambridge, USA
Natural sounds are structured on many time-scales. A typical segment of speech,
for example, contains features that span four orders of magnitude: Sentences
(~ 1 s); phonemes (~ 10^-1 s); glottal pulses (~ 10^-2 s); and formants (~ 10^-3 s)
The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscience-inspired algorithms which solve similar tasks and to compare the properties of
these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct
an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract
short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for
natural sounds that captures structure across a wide range of time-scales, and to
provide efficient learning and inference algorithms. We demonstrate the success
of this approach on a missing data task.