In this paper, we propose a deep neural network (DNN) ensemble for reducing artificial noise in speech bandwidth extension (BWE). The proposed DNN ensemble consists of three DNN models; one is a classification model, and the other two are regression models. When estimating sub-band energies of the high-frequency region using sequential DNNs in a frequency domain, the over-estimation of sub-band energies causes annoying artificial noise. To mitigate this artificial noise, we design a DNN classification model that can classify over-estimation frames against normal frames. Then, we separately develop two DNN regression models using half of the entire training set and a limited training set built with over-estimation frames and some normal frames to improve the performance at the over-estimation frames. Since the outputs of the classification model are probabilities of either a normal frame or an over-estimation frame, respectively, two regression models are adjustably combined by using the probabilistic weights; thus, the final output of the DNN ensemble is the weighted sum of two estimated sub-band energies. As a result, artificial noise is significantly reduced, yielding improved speech quality. The proposed method is objectively and subjectively evaluated by comparing it with conventional approaches.

在本文中，我们提出了一种用于减少语音带宽扩展（BWE）中的人工噪声的深度神经网络（DNN）集成。拟议的DNN集成由三个DNN模型组成。一个是分类模型，另外两个是回归模型。当在频域中使用顺序DNN估计高频区域的子带能量时，子带能量的过高估计会引起烦人的人工噪声。为了减轻这种人为噪声，我们设计了一种DNN分类模型，该模型可以将高估帧与正常帧进行分类。然后，我们分别使用整个训练集的一半和使用过高估计帧和一些正常帧构建的有限训练集分别开发两个DNN回归模型，以提高过高估计帧的性能。由于分类模型的输出分别是正常框架或高估框架的概率，因此使用概率权重可调整地组合两个回归模型。因此，DNN合奏的最终输出是两个估计的子带能量的加权和。结果，大大降低了人工噪声，从而提高了语音质量。通过与常规方法进行比较，客观，主观地评估了所提出的方法。改善语音质量。通过与常规方法进行比较，客观，主观地评估了所提出的方法。改善语音质量。通过与常规方法进行比较，可以客观，主观地评估所提出的方法。