Pitch-informed solo and accompaniment separation towards its use in music education applications
We present a system for the automatic separation of solo instruments and music accompaniment in polyphonic music recordings. Our approach is based on a pitch detection front-end and a tone-based spectral estimation. We assess the plausibility of using sound separation technologies to create practice material in a music education context. To better understand the sound separation quality requirements in music education, a listening test was conducted to determine the most perceptually relevant signal distortions that need to be improved. Results from the listening test show that solo and accompaniment tracks pose different quality requirements and should be optimized differently. We propose and evaluate algorithm modifications to better understand their effects on objective perceptual quality measures. Finally, we outline possible ways of optimizing our separation approach to better suit the requirements of music education applications.