Sparse Independent Vector Analysis (SparseAuxIVA)¶
- pyroomacoustics.bss.sparseauxiva.sparseauxiva(X, S=None, n_src=None, n_iter=20, proj_back=True, W0=None, model='laplace', return_filters=False, callback=None)¶
Implementation of sparse AuxIVA algorithm for BSS presented in
J. Janský, Z. Koldovský, and N. Ono, A computationally cheaper method for blind speech separation based on AuxIVA and incomplete demixing transform, Proc. IEEE, IWAENC, pp. 1-5, Sept. 2016.
- Parameters:
X (ndarray (nframes, nfrequencies, nchannels)) – STFT representation of the signal
n_src (int, optional) – The number of sources or independent components
S (ndarray (k_freq)) – Indexes of active frequency bins for sparse AuxIVA
n_iter (int, optional) – The number of iterations (default 20)
proj_back (bool, optional) – Scaling on first mic by back projection (default True)
W0 (ndarray (nfrequencies, nchannels, nchannels), optional) – Initial value for demixing matrix
model (str) – The model of source distribution ‘gauss’ or ‘laplace’ (default)
return_filters (bool) – If true, the function will return the demixing matrix too
callback (func) – A callback function called every 10 iterations, allows to monitor convergence
- Returns:
Returns an (nframes, nfrequencies, nsources) array. Also returns
the demixing matrix (nfrequencies, nchannels, nsources)
if
return_values
keyword is True.