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Gamma Chains priors for audio processing

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Useful models for nonstationary audio sources can be obtained by modelling the dependencies in the time-frequency representations of signals. Gamma Markov chains enable us to introduce such dependencies. We used IGM Cs to model the sources in denoising and single channel source separation applications. We will discuss the pros and cons of various inference schemata such as Gibbs sampling, variational Bayes and sequential Monte Carlo.

This talk is part of the Audio and Music Processing (AMP) Reading Group series.

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