  We propose a novel unsupervised singing voice detection method which use
single-channel Blind Audio Source Separation (BASS) algorithm as a preliminary
step. To reach this goal, we investigate three promising BASS approaches which
operate through a morphological filtering of the analyzed mixture spectrogram.
The contributions of this paper are manyfold. First, the investigated BASS
methods are reworded with the same formalism and we investigate their
respective hyperparameters by numerical simulations. Second, we propose an
extension of the KAM method for which we propose a novel training algorithm
used to compute a source-specific kernel from a given isolated source signal.
Second, the BASS methods are compared together in terms of source separation
accuracy and in terms of singing voice detection accuracy when they are used in
our new singing voice detection framework. Finally, we do an exhaustive singing
voice detection evaluation for which we compare both supervised and
unsupervised singing voice detection methods. Our comparison explores different
combination of the proposed BASS methods with new features such as the new
proposed KAM features and the scattering transform through a machine learning
framework and also considers convolutional neural networks methods.
