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We propose a fully probabilistic model for source-filter based single channel source separation. In particular, we perform separation in a sequential manner, where we estimate the source-driven aspects by a factorial HMM used for multi-pitch estimation. Afterwards, these pitch tracks are combined with the vocal tract filter model to form an utterance dependent model. Additionally, we introduce a gain...
In most current model based single channel separation techniques, it is assumed that the recording conditions are identical in the training phase and application phase. In this paper, we consider a general case in which training data and application data have different levels of energy and a technique is proposed to estimate the sources' gains which are required for the separation process. We use...
We present a generalized approach to speaker dependent model-based single channel speech separation techniques in which a priori knowledge of the underlying speakers is used to separate speech signals. For this purpose, we add an identification stage by which we first identify the underlying speakers in the mixture and then use the identified speakers' model to separate speech signals. The proposed...
In this paper, we present a new technique for separating two speech signals from a single recording. For this purpose, we decompose the speech signal into the excitation signal and the vocal tract function and then estimate the components from the mixed speech using a hybrid model. We first express the probability density function (PDF) of the mixed speech's log spectral vectors in terms of the PDFs...
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