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20232026
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cs.SD2026

FlowGRPO for Vocal Separation: From Reward Analysis to Reward Design

Lishi Zuo, Chong-Xin Gan, Zezhong Jin +3

The Flow Matching objective supervises the velocity field at intermediate states, whereas waveform metrics are evaluated after decoding and may be non-differentiable. To align Flow…

cs.SD2026

Decoupled Latent Flow Matching for Few-Step Joint Vocal-Accompaniment Separation

Lishi Zuo, Youzhi Tu, Lu Yi +4

Generative modeling provides a flexible way to model mixture-conditioned source distributions, but iterative diffusion and flow matching models are costly for long music signals. T…

cs.SD2026

Beyond Residual Connections: Manifold-Constrained Hyper-Connections for Robust Speaker Representation Learning

Zezhong Jin, Xiaoyu Wang, Zhe Li +4

Residual connections are fundamental to deep speaker recogni- tion models, such as ECAPA-TDNN and ResNet. However, standard identity mapping limits information flow to a sin- gle p…

cs.SD2026

LISE : Listenable Interpretable Speaker Embeddings

Xiaoliang Wu, Chongxin Gan, Ke Liu +2

Deep neural network-based automatic speaker verification (ASV) systems achieve impressive performance but their embedding representations remain opaque, lacking a structured and pe…

cs.SD2026

Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Training

Yanru Wu, Jianning Wang, Chongxin Gan +1

Training general-purpose Audio Large Language Models (ALLMs) across diverse datasets is essential for holistic audio understanding, yet it faces significant challenges due to datas…