Extracting singing voice from music recordings by cascading audio decomposition techniques

The problem of extracting singing voice from music recordings has received increasing research interest in recent years. Many proposed decomposition techniques are based on one of the following two strategies. The first approach is to directly decompose a given music recording into one component for the singing voice and one for the accompaniment by exploiting knowledge about specific characteristics of singing voice. Procedures following the second approach disassemble the recording into a large set of fine-grained components, which are classified and reassembled afterwards to yield the desired source estimates. In this paper, we propose a novel approach that combines the strengths of both strategies. We first apply different audio decomposition techniques in a cascaded fashion to disassemble the music recording into a set of mid-level components. This decomposition is fine enough to model various characteristics of singing voice, but coarse enough to keep an explicit semantic meaning of the components. These properties allow us to directly reassemble the singing voice and the accompaniment from the components. Our objective and subjective evaluations show that this strategy can compete with state-of-the-art singing voice separation algorithms and yields perceptually appealing results.