Amplitude demodulation is a widely used tool of signal processing. It involves decomposing a sigal, y(t), into a product of a slowly varying positive modulator, m(t), and a quickly varying carrier, c(t). That is, y(t) = m(t)c(t). The representation is useful because the modulator represents the local energy in the signal, whilst the carrier represents the fine-structure. Often the local energy and fine-structure contain very different information, for instance the identity of a particular speaker versus the content of what they are saying. Applications of demodulation include hearing aids, cochlear implants for the deaf, speech recognition, and information retrieval. Unfortunately, standard deterministic algorithms for performing amplitude demodulation are flawed in various ways. We have therefore developed an alternative approach, based on probabilistic methods used in machine learning, which resolves a number of these problems, but these benefits come with a larger computational cost.
This page collects together publications and code for performing probabilistic amplitude demodulation.
Greg Sell and Malcolm Slaney have developed a related convex optimisation approach to demodulation. For papers and code see their web page. For a formal proof that convex demodulation is a version of PAD see Demodulation as Probabilistic Inference, 2011.
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