Source Separation for A Cappella Music

• URL: http://arxiv.org/abs/2509.26580v1
• Date: Tue, 30 Sep 2025 17:39:40 GMT
• Title: Source Separation for A Cappella Music
• Authors: Luca A. Lanzendörfer, Constantin Pinkl, Florian Grötschla,
• Abstract summary: We study the task of multi-singer separation in a cappella music, where the number of active singers varies across mixtures.<n>To separate singers, we introduce SepACap, an adaptation of SepReformer, a state-of-the-art speaker separation model architecture.<n> Experiments on the JaCappella dataset demonstrate that our approach achieves state-of-the-art performance in both full-ensemble and subset singer separation scenarios.
• Score: 11.877895671677964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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