Single-channel singing voice separation has been considered a difficult task, as it requires predicting two different audio sources independently from mixed vocal and instrument sounds recorded by a single microphone. We propose a new singing voice separation approach based on the curriculum learning framework, in which learning is started with only easy examples and then task difficulty is gradually increased. In this study, we regard the data providing obviously dominant characteristics of a single source as an easy case and the other data as a difficult case. To quantify the dominance property between two sources, we define a dominance factor that determines a difficulty level according to relative intensity between vocal sound and instrument sound. If a given data is determined to provide obviously dominant characteristics of a single source according to the factor, it is regarded as an easy case; otherwise, it belongs to a difficult case. Early stages in the learning focus on easy cases, thus allowing rapidly learning overall characteristics of each source. On the other hand, later stages handle difficult cases, allowing more careful and sophisticated learning. In experiments conducted on three song datasets, the proposed approach demonstrated superior performance compared to the conventional approaches.
Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask
