用量子傅里叶变换提升语音降噪，效果更好且不增加计算开销。
Quantum Fourier Transform Based Denoising: Unitary Filtering for Enhanced Speech Clarity
- 用量子傅里叶变换替代传统FFT，保持相位一致性和能量守恒
- 在低信噪比下提升15 dB，且减少伪影生成
- 适合需要高效高保真语音处理的场景
本文提出一种基于量子傅里叶变换（QFT）的语音降噪框架，将其融入经典音频增强流程。与传统的快速傅里叶变换（FFT）方法不同，QFT具备全局相位相干性与能量保持特性，能更有效区分语音与噪声。该方法将Wiener滤波和谱减法中的FFT替换为QFT算子，确保超参数设置一致，实现公平比较。在清洁语音、合成音调及多种信噪比（SNR）条件下的噪声混合信号上进行实验，结果表明，降噪性能显著提升，最高可实现15 dB的信噪比增益，同时降低伪影生成。实验验证了该方法在低SNR和非平稳噪声场景下的鲁棒性，且无需额外计算开销，展现出量子增强语音处理的可扩展潜力。
原文摘要 · Abstract (English)
This paper introduces a quantum-inspired denoising framework that integrates the Quantum Fourier Transform (QFT) into classical audio enhancement pipelines. Unlike conventional Fast Fourier Transform (FFT) based methods, QFT provides a unitary transformation with global phase coherence and energy preservation, enabling improved discrimination between speech and noise. The proposed approach replaces FFT in Wiener and spectral subtraction filters with a QFT operator, ensuring consistent hyperparameter settings for fair comparison. Experiments on clean speech, synthetic tones, and noisy mixtures across diverse signal to noise ratio (SNR) conditions, demonstrate statistically significant gains in SNR, with up to 15 dB improvement and reduced artifact generation. Results confirm that QFT based denoising offers robustness under low SNR and nonstationary noise scenarios without additional computational overhead, highlighting its potential as a scalable pathway toward quantum-enhanced speech processing.
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