R. Turner - Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference

 
In this talk I will discuss a new method for decomposing a signal into a sum of amplitude and frequency modulated sinusoids. Such representations are often used for producing stimuli for scientific experiments e.g. vocoded signals, chimeric sounds, or sinusoidal speech. Moreover, the representation can form the most primitive stage of a computational auditory scene analysis system.

Classically, there are two main ways to realise such a decomposition. The first is subband demodulation where a signal is first passed through a filter bank, before being demodulated using, for example, the Hilbert Transform.  The second approach is sinusoidal modelling which uses a set of heuristics to track the sinusoidal components present in a signal (e.g. the McAulay-Quatieri algorithm).

I will introduce a new approach, which uses probabilistic inference to realise the representation. We show that the new method has several advantages over the classical approaches, suffering fewer artifacts as well as handling noise and missing data naturally. The main drawback is that the new method is computationally more expensive than the classical approaches.

This is joint work with Maneesh Sahani.