DNN-Based Speech Enhancement Using Soft Audible Noise Masking for Wind Noise Reduction

This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.

基于深度学习的缺失数据故障诊断方法研究

由于不完整观测数据会严重影响故障的诊断结果,针对缺失率增大、观察变量之间相关系数降低及传统插补方法无法有效提取数据潜在特征等问题,本文提出了一种基于深度学习的插补方法来估计缺失数据。实验验证了该方法的有效性。