Source Separation for A Cappella Music
This addresses source separation for a cappella music, a domain-specific task, with incremental improvements over existing methods.
The paper tackles the problem of separating multiple singers in a cappella music, where the number of singers varies, by introducing SepACap, an adapted model that achieves state-of-the-art performance on the JaCappella dataset.
In this work, we study the task of multi-singer separation in a cappella music, where the number of active singers varies across mixtures. To address this, we use a power set-based data augmentation strategy that expands limited multi-singer datasets into exponentially more training samples. To separate singers, we introduce SepACap, an adaptation of SepReformer, a state-of-the-art speaker separation model architecture. We adapt the model with periodic activations and a composite loss function that remains effective when stems are silent, enabling robust detection and separation. Experiments on the JaCappella dataset demonstrate that our approach achieves state-of-the-art performance in both full-ensemble and subset singer separation scenarios, outperforming spectrogram-based baselines while generalizing to realistic mixtures with varying numbers of singers.