Score-Informed Voice Separation For Piano Recordings

The decomposition of a monaural audio recording into musically meaningful sound sources or voices constitutes a fundamental problem in music information retrieval. In this paper, we consider the task of separating a monaural piano recording into two sound sources (or voices) that correspond to the left hand and the right hand. Since in this scenario the two sources share many physical properties, sound separation approaches identifying sources based on their spectral envelope are hardly applicable. Instead, we propose a score-informed approach, where explicit note events specified by the score are used to parameterize the spectrogram of a given piano recording. This parameterization then allows for constructing two spectrograms considering only the notes of the left hand and the right hand, respectively. Finally, inversion of the two spectrograms yields the separation result. First experiments show that our approach, which involves high-resolution music synchronization and parametric modeling techniques, yields good results for realworld non-synthetic piano recordings.