  Singing voice conversion is a task to convert a song sang by a source singer
to the voice of a target singer. In this paper, we propose using a parallel
data free, many-to-one voice conversion technique on singing voices. A phonetic
posterior feature is first generated by decoding singing voices through a
robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent
Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM)
structure is used to model the mapping from person-independent content to the
acoustic features of the target person. F0 and aperiodic are obtained through
the original singing voice, and used with acoustic features to reconstruct the
target singing voice through a vocoder. In the obtained singing voice, the
targeted and sourced singers sound similar. To our knowledge, this is the first
study that uses non parallel data to train a singing voice conversion system.
Subjective evaluations demonstrate that the proposed method effectively
converts singing voices.
