A method for processing a plurality of signals x 1 (t)...x 4 (t) acquired using a corresponding plurality of acoustic sensors at a user device, said signals having parts from a plurality of spatially distributed acoustic sources, the method comprising: computing, using a processor at the user device, time-dependent spectral characteristics X(f,n) from at least one signal of the plurality of acquired signals, the time-dependent spectral characteristics comprising a plurality of components, each component associated with a respective pair of frequency (f) and time (n) values; computing, using the processor at the user device, direction estimates D(f,n) from at least two signals of the plurality of acquired signals, each computed component of the time-dependent spectral characteristics having a corresponding direction estimate (d) of the computed directions estimates; combining the computed time-dependent spectral characteristics and the computed direction estimates to form a data structure representing a distribution p(f,n,d) indexed by frequency (f), time (n), and direction (d); forming an approximation q(f,n,d) of the distribution p(f,n,d), where the distribution q(f,n,d) has a hidden multiple-source structure assuming that the at least one signal of the plurality of acquired signals was generated by a number of distinct acoustic sources indexed by s=1,..., S and each acoustic source is associated with a number of prototype frequency distributions indexed by z=1,..., Z so that the approximation can be factorized into constituent parts; performing a plurality of iterations of adjusting components of a model of the approximation q(f,n,d) to best match the distribution p(f,n,d); and computing a mask function M(f,n) for separating a contribution of a selected acoustic source (s*) of the plurality of spatially distributed acoustic sources from at least one signal of the plurality of acquired signals using the constituent parts of the approximation corresponding to the selected acoustic source (s*). computing, using a processor at the user device, time-dependent spectral characteristics X(f,n) from at least one signal of the plurality of acquired signals, the time-dependent spectral characteristics comprising a plurality of components, each component associated with a respective pair of frequency (f) and time (n) values; computing, using the processor at the user device, direction estimates D(f,n) from at least two signals of the plurality of acquired signals, each computed component of the time-dependent spectral characteristics having a corresponding direction estimate (d) of the computed directions estimates; combining the computed time-dependent spectral characteristics and the computed direction estimates to form a data structure representing a distribution p(f,n,d) indexed by frequency (f), time (n), and direction (d); forming an approximation q(f,n,d) of the distribution p(f,n,d), where the distribution q(f,n,d) has a hidden multiple-source structure assuming that the at least one signal of the plurality of acquired signals was generated by a number of distinct acoustic sources indexed by s=1,..., S and each acoustic source is associated with a number of prototype frequency distributions indexed by z=1,..., Z so that the approximation can be factorized into constituent parts; performing a plurality of iterations of adjusting components of a model of the approximation q(f,n,d) to best match the distribution p(f,n,d); and computing a mask function M(f,n) for separating a contribution of a selected acoustic source (s*) of the plurality of spatially distributed acoustic sources from at least one signal of the plurality of acquired signals using the constituent parts of the approximation corresponding to the selected acoustic source (s*).