标题： 基于DNN的声源增强以提高客观声音质量评估分数 显示英文标题

标题： DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score

摘要： 我们提出了一种基于深度神经网络（DNN）的源增强训练方法，以提高客观声音质量评估（OSQA）分数，如语音质量的感知评估（PESQ）。 在许多传统研究中，DNN被用作映射函数来估计时频掩码，并被训练以最小化可分析处理的目标函数，如均方误差（MSE）。 由于OSQA分数已被广泛用于声音质量评估，构建能够提高OSQA分数的DNN比使用最小MSE来生成高质量输出信号更好。 然而，由于大多数OSQA分数不可分析处理，\textit{即}，它们是黑箱，无法通过简单应用反向传播计算目标函数的梯度。 为了计算基于OSQA的目标函数的梯度，我们基于\textit{黑盒优化}制定了一种DNN优化方案，该方案用于训练玩游戏的计算机。 对于黑箱优化方案，我们采用策略梯度方法，基于采样算法计算梯度。 为了使用采样算法模拟输出信号，DNN用于估计最大化OSQA分数的输出信号的概率密度函数。 OSQA分数从模拟的输出信号中计算得出，并且DNN被训练以增加生成实现高OSQA分数的模拟输出信号的概率。 通过多项实验，我们发现即使未最小化MSE，应用所提出的方法也能显著提高OSQA分数。 显示英文摘要

摘要： We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for sound-quality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create high-quality output signals. However, since most OSQA scores are not analytically tractable, \textit{i.e.}, they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of \textit{black-box optimization}, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized.