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CIS-BWE: Chaos-Informed Speech Bandwidth Extension
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:48
摘要
We design CIS-BWE, a novel adversarial Bandwidth Extension (BWE) framework that introduces two chaos-informed discriminators - Multi-Resolution Lyapunov Discriminator (MRLD) and Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) - for capturing the deterministic chaos from speech. MRLD exploits Lyapunov exponents to capture nonlinear chaotic fluctuations. MSDFA exploits detrended fluctuation analysis to quantify fractal-like, long-range temporal chaotic correlations. To the best of our knowledge, MRLD and MSDFA are included here for the first time with a complex-valued adversarial network to explore the chaotic study of speech reconstruction. We also introduce a novel complex-valued and dual-stream generator, which uses our newly proposed ConformerNeXt as a core block with Lattice interactions, acting as a gating mechanism by enabling controlled mixing of information across streams. We extensively optimize our design across five resolutions and use depth-wise separable convolutions to make our model lightweight yet powerful. Our CIS-BWE is tested with a de facto English and French dataset for clean and noisy speech for generalization. It achieves better performance across a total of nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task.
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记录 #1 · 2026-07 · 最新观测版本
CIS-BWE: Chaos-Informed Speech Bandwidth Extension
We design CIS-BWE, a novel adversarial Bandwidth Extension (BWE) framework that introduces two chaos-informed discriminators - Multi-Resolution Lyapunov Discriminator (MRLD) and Multi-Scale Detrended Fractal Analysis Discriminator (MSDFA) - for capturing the deterministic chaos from speech. MRLD exploits Lyapunov exponents to capture nonlinear chaotic fluctuations. MSDFA exploits detrended fluctuation analysis to quantify fractal-like, long-range temporal chaotic correlations. To the best of our knowledge, MRLD and MSDFA are included here for the first time with a complex-valued adversarial network to explore the chaotic study of speech reconstruction. We also introduce a novel complex-valued and dual-stream generator, which uses our newly proposed ConformerNeXt as a core block with Lattice interactions, acting as a gating mechanism by enabling controlled mixing of information across streams. We extensively optimize our design across five resolutions and use depth-wise separable convolutions to make our model lightweight yet powerful. Our CIS-BWE is tested with a de facto English and French dataset for clean and noisy speech for generalization. It achieves better performance across a total of nine subjective and objective evaluation metrics with a 40x reduction in discriminator size and overall 0.5x fewer parameters, establishing a new baseline in the BWE task.
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