ACappellaSet: A Multilingual A Cappella Dataset for Source Separation and AI-assisted Rehearsal Tools

A cappella music presents unique challenges for source separation due to its diverse vocal styles and the presence of vocal percussion. Current a cappella datasets are limited in size and diversity, hindering the development of robust source separation models. In this paper, we present \textit{ACappellaSet}, a collection of 55 professionally recorded a cappella songs performed by three professional groups. In addition, we present experimental results showing that fine-tuning Demucs on ACappellaSet substantially improves vocal percussion (VP) separation, raising VP SDR from 5.22~dB to 7.62~dB. Finally, we discuss future work on AI-driven dataset augmentation and supporting tools for asynchronous a cappella rehearsals.