Deep Neural Networks (DNNs) often struggle to suppress noise at low signal-to-noise ratios (SNRs). This paper addresses speech enhancement in scenarios dominated by harmonic noise and proposes a framework that integrates cyclostationarity-aware preprocessing with lightweight DNN-based denoising. A cyclic minimum power distortionless response (cMPDR) spectral beamformer is used as a preprocessing block. It exploits the spectral correlations of cyclostationary noise to suppress harmonic components prior to learning-based enhancement and does not require modifications to the DNN architecture. The proposed pipeline is evaluated in a single-channel setting using two DNN architectures: a simple and lightweight convolutional recurrent neural network (CRNN), and a state-of-the-art model, namely ultra-low complexity network (ULCNet). Experiments on synthetic data and real-world recordings dominated by rotating machinery noise demonstrate consistent improvements over end-to-end DNN baselines, particularly at low SNRs. Remarkably, a parameter-efficient CRNN with cMPDR preprocessing surpasses the performance of the larger ULCNet operating on raw or Wiener-filtered inputs. These results indicate that explicitly incorporating cyclostationarity as a signal prior is more effective than increasing model capacity alone for suppressing harmonic interference.

翻译：深度神经网络（DNNs）在低信噪比（SNRs）条件下往往难以有效抑制噪声。本文针对谐波噪声主导场景下的语音增强问题，提出了一种将循环平稳性感知预处理与轻量级基于DNN的去噪相结合的框架。该框架采用循环最小功率无失真响应（cMPDR）谱波束成形器作为预处理模块。该模块利用循环平稳噪声的谱相关性，在学习式增强前抑制谐波分量，且无需修改DNN架构。所提出的流程在单通道设置下使用两种DNN架构进行评估：一种简单轻量的卷积循环神经网络（CRNN），以及一种先进模型，即超低复杂度网络（ULCNet）。在合成数据及以旋转机械噪声为主的真实世界录音上的实验表明，该方法相较于端到端DNN基线模型取得了持续的性能提升，尤其在低信噪比条件下。值得注意的是，经过cMPDR预处理的参数高效型CRNN，其性能甚至超越了在原始或维纳滤波输入上运行的更大规模ULCNet。这些结果表明，在抑制谐波干扰方面，显式引入循环平稳性作为信号先验，比单纯增加模型容量更为有效。