Choral Music Separation

Choral music separation aims to computationally isolate individual vocal parts (e.g., soprano, alto, tenor, bass) from a mixed recording. Recent research focuses on developing robust deep learning models, including both supervised approaches using increasingly larger datasets (sometimes augmented with synthesized data) and unsupervised methods leveraging parametric source models to overcome data scarcity limitations. These advancements are significant because they enable more detailed analysis of choral music and offer potential applications in music transcription, restoration, and personalized listening experiences.