论文标题
基于深度学习的单端客观质量措施，用于时间尺度修改音频
Deep Learning-Based Single-Ended Objective Quality Measures for Time-Scale Modified Audio
论文作者
论文摘要
对时间尺度修改（TSM）处理的音频的客观评估正在看到感兴趣的复兴。最近，使用标记的时标音频数据集来训练客观的TSM评估措施。该措施是对音频质量的感知评估的扩展，以及所需的参考和测试信号。在本文中，提出了不需要参考信号的时间缩放音频的两项单端客观质量度量。数据驱动的特征是由卷积神经网络（CNN）或双向封盖复发单元（BGRU）网络创建的，并馈送到完全连接的网络以预测主观的平均意见分数。提出的CNN和BGRU测量值的平均均方根误差为0.608和0.576，平均Pearson相关性分别为0.771和0.794。提出的措施用于评估TSM算法，并为16个TSM实施提供了比较。客观度量可在https://www.github.com/zygurt/tsm上获得。
Objective evaluation of audio processed with Time-Scale Modification (TSM) is seeing a resurgence of interest. Recently, a labelled time-scaled audio dataset was used to train an objective measure for TSM evaluation. This DE measure was an extension of Perceptual Evaluation of Audio Quality, and required reference and test signals. In this paper, two single-ended objective quality measures for time-scaled audio are proposed that do not require a reference signal. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network and fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average Root Mean Squared Error of 0.608 and 0.576, and a mean Pearson correlation of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. The objective measure is available at https://www.github.com/zygurt/TSM.
