  Singing voice detection (SVD), to recognize vocal parts in the song, is an
essential task in music information retrieval (MIR). The task remains
challenging since singing voice varies and intertwines with the accompaniment
music, especially for some complicated polyphonic music such as choral music
recordings. To address this problem, we investigate singing voice detection
while discarding the interference from the accompaniment. The proposed SVD has
two steps: i. The singing voice separation (SVS) technique is first utilized to
filter out the singing voice's potential part coarsely. ii. Upon the continuity
of vocal in the time domain, Long-term Recurrent Convolutional Networks (LRCN)
is used to learn compositional features. Moreover, to eliminate the outliers,
we choose to use a median filter for time-domain smoothing. Experimental
results show that the proposed method outperforms the existing state-of-the-art
works on two public datasets, the Jamendo Corpus and the RWC pop dataset.
