Abstract: An input signal, in the form of a sequence of feature vectors, is transformed to an output signal by first storing parameters of a model of the input signal in a memory. Using the vectors and the parameters, a sequence of vectors of hidden variables is inferred. There is at least one vector hn of hidden variables hi,n for each feature vector xn, and each hidden variable is nonnegative. The output signal is generated using the feature vectors, the vectors of hidden variables, and the parameters. Each feature vector xn is dependent on at least one of the hidden variables hi,n for the same n. The hidden variables are related according to h i , n = ? j , l ? ? c i , j , l ? ? l , n ? h j , n - 1 , where j and l are summation indices. The parameters include non-negative weights ci,j,l, and ?l,n are independent non-negative random variables.

Abstract: A source separation system is provided. The system includes a plurality of sources being subjected to an automatic source separation via a joint use of segmental information and spatial diversity. The system further includes a set of spectral shapes representing spectral diversity derived from the automatic source separation being automatically provided. The system still further includes a plurality of mixing parameters derived from the set of spectral shapes. Within a sampling range, a triplet is processed wherein a reconstruction of a Short Term Fourier Transform (STFT) corresponding to a source triplet among the set of triplets is performed.