用小模型控制经典降噪算法，实现低失真高降噪。
Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
- 用小型神经网络动态调控多通道维纳滤波参数
- 相同算力下比多个基线方法在主观和客观指标上更优
- 适合对实时性与语音保真度要求高的场景
噪声抑制与语音失真是多通道语音增强算法设计中的两个关键挑战。尽管神经网络模型在降噪性能上达到顶尖水平，其非线性操作常导致较高语音失真。相反，经典信号处理方法如参数化多通道维纳滤波（PMWF）提供了明确的抑制/失真权衡控制机制。本文提出NeuralPMWF，通过一个低延迟、低计算量的神经网络完全控制PMWF，构建出复杂度低、降噪强且失真小的系统。实验表明，在相似计算资源下，该方法在感知质量和客观指标上显著优于多个竞争基线。
原文摘要 · Abstract (English)
Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models have achieved state-of-the-art noise suppression, their non-linear operations often introduce high speech distortion. Conversely, classical signal processing algorithms such as the Parameterized Multi-channel Wiener Filter ( PMWF) beamformer offer explicit mechanisms for controlling the suppression/distortion trade-off. In this work, we present NeuralPMWF, a system where the PMWF is entirely controlled using a low-latency, low-compute neural network, resulting in a low-complexity system offering high noise reduction and low speech distortion. Experimental results show that our proposed approach results in significantly better perceptual and objective speech enhancement in comparison to several competitive baselines using similar computational resources.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。