1. A method, comprising: performing, by one or more computing devices: generating a source model for a sound source based, at least in part, on a training signal, the source model including a plurality of spectral dictionaries corresponding to the training signal, a given segment of the training signal being represented by a given one of the plurality of spectral dictionaries, the given segment of the training signal being less than the training signal in whole, each of the plurality of spectral dictionaries including at least one spectral component, and the source model further including probabilities of transition among the plurality of spectral dictionaries; receiving a mixed signal including a combination of a signal of interest with a noise signal, the signal of interest being emitted by the sound source; in response to receiving an instruction to separate the signal of interest from the noise signal, generating a mixture model for the mixed signal using, at least in part, the source model, the mixture model including a plurality of mixture weights corresponding to the combination of the signal of interest and the noise signal, and a spectral dictionary corresponding to the noise signal; constructing a mask for the mixed signal based, at least in part, on the mixture model; and applying the mask to the mixture signal to separate the signal of interest from the noise signal.
1. A method, comprising: performing, by one or more computing devices: generating a source model for a sound source based, at least in part, on a training signal, the source model including a plurality of spectral dictionaries corresponding to the training signal, a given segment of the training signal being represented by a given one of the plurality of spectral dictionaries, the given segment of the training signal being less than the training signal in whole, each of the plurality of spectral dictionaries including at least one spectral component, and the source model further including probabilities of transition among the plurality of spectral dictionaries; receiving a mixed signal including a combination of a signal of interest with a noise signal, the signal of interest being emitted by the sound source; in response to receiving an instruction to separate the signal of interest from the noise signal, generating a mixture model for the mixed signal using, at least in part, the source model, the mixture model including a plurality of mixture weights corresponding to the combination of the signal of interest and the noise signal, and a spectral dictionary corresponding to the noise signal; constructing a mask for the mixed signal based, at least in part, on the mixture model; and applying the mask to the mixture signal to separate the signal of interest from the noise signal. 2. The method of claim 1 , wherein the source model is a non-negative hidden Markov model (N-HMM).