Singing Voice Separation

Singing voice separation aims to isolate vocal tracks from musical mixtures, a crucial task in music information retrieval and audio production. Current research focuses on improving the accuracy and efficiency of separation, employing deep learning models like U-Net variations, diffusion models, and cascade architectures that jointly optimize for voice separation and pitch estimation. These advancements are driven by the need for high-quality isolated vocals in applications such as karaoke generation, film post-production, and music analysis, impacting both the development of new algorithms and the creation of more effective datasets for evaluation. The field is also exploring methods to handle complex scenarios like duets and polyphonic music, moving beyond simple instrument/vocal separation.