  Separating a singing voice from its music accompaniment remains an important
challenge in the field of music information retrieval. We present a unique
neural network approach inspired by a technique that has revolutionized the
field of vision: pixel-wise image classification, which we combine with cross
entropy loss and pretraining of the CNN as an autoencoder on singing voice
spectrograms. The pixel-wise classification technique directly estimates the
sound source label for each time-frequency (T-F) bin in our spectrogram image,
thus eliminating common pre- and postprocessing tasks. The proposed network is
trained by using the Ideal Binary Mask (IBM) as the target output label. The
IBM identifies the dominant sound source in each T-F bin of the magnitude
spectrogram of a mixture signal, by considering each T-F bin as a pixel with a
multi-label (for each sound source). Cross entropy is used as the training
objective, so as to minimize the average probability error between the target
and predicted label for each pixel. By treating the singing voice separation
problem as a pixel-wise classification task, we additionally eliminate one of
the commonly used, yet not easy to comprehend, postprocessing steps: the Wiener
filter postprocessing.
  The proposed CNN outperforms the first runner up in the Music Information
Retrieval Evaluation eXchange (MIREX) 2016 and the winner of MIREX 2014 with a
gain of 2.2702 ~ 5.9563 dB global normalized source to distortion ratio (GNSDR)
when applied to the iKala dataset. An experiment with the DSD100 dataset on the
full-tracks song evaluation task also shows that our model is able to compete
with cutting-edge singing voice separation systems which use multi-channel
modeling, data augmentation, and model blending.
