  This paper presents a new method of singing voice analysis that performs
mutually-dependent singing voice separation and vocal fundamental frequency
(F0) estimation. Vocal F0 estimation is considered to become easier if singing
voices can be separated from a music audio signal, and vocal F0 contours are
useful for singing voice separation. This calls for an approach that improves
the performance of each of these tasks by using the results of the other. The
proposed method first performs robust principal component analysis (RPCA) for
roughly extracting singing voices from a target music audio signal. The F0
contour of the main melody is then estimated from the separated singing voices
by finding the optimal temporal path over an F0 saliency spectrogram. Finally,
the singing voices are separated again more accurately by combining a
conventional time-frequency mask given by RPCA with another mask that passes
only the harmonic structures of the estimated F0s. Experimental results showed
that the proposed method significantly improved the performances of both
singing voice separation and vocal F0 estimation. The proposed method also
outperformed all the other methods of singing voice separation submitted to an
international music analysis competition called MIREX 2014.
