  Musical audio is generally composed of three physical properties: frequency,
time and magnitude. Interestingly, human auditory periphery also provides
neural codes for each of these dimensions to perceive music. Inspired by these
intrinsic characteristics, a frequency-temporal attention network is proposed
to mimic human auditory for singing melody extraction. In particular, the
proposed model contains frequency-temporal attention modules and a selective
fusion module corresponding to these three physical properties. The frequency
attention module is used to select the same activation frequency bands as did
in cochlear and the temporal attention module is responsible for analyzing
temporal patterns. Finally, the selective fusion module is suggested to
recalibrate magnitudes and fuse the raw information for prediction. In
addition, we propose to use another branch to simultaneously predict the
presence of singing voice melody. The experimental results show that the
proposed model outperforms existing state-of-the-art methods.
