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In many audio processing tasks, such as source separation, denoising or compression, it is crucial to construct realistic and flexible models to capture the physical properties of audio signals. This can be accomplished in the Bayesian framework through the use of appropriate prior distributions. In this paper, we describe a class of prior models called Gamma Markov random fields (GMRFs) to model...
We propose a prior structure for single-channel audio source separation using non-negative matrix factorisation. For the tonal and percussive signals, the model assigns different prior distributions to the corresponding parts of the template and excitation matrices. This partitioning enables not only more realistic modelling, but also a deterministic way to group the components into sources. This...
Audio processing tasks, such as source separation or denoising, require the construction of realistic models that reflect physical properties of audio signals. In this paper, we modelled the variances of time-frequency coefficients of audio signals with gamma Markov random fields (GMRFs) so that the dependencies between coefficients are captured. There is positive correlation between consecutive variance...
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