InSE-NET：一种基于CNN的感知编码音频质量模型
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN
摘要：
自动编码音频质量评估是一项重要的任务，其进展受到人类注释的缺乏，对看不见的编解码器，比特率，内容类型的泛化能力差以及现有方法缺乏灵活性的阻碍。ViSQOL v3（ViV 3）是典型的人类感知相关指标之一，已被证明与人类评定的质量分数具有高度相关性。在本研究中，我们采取措施，通过完全利用以编程方式生成的、基于专家领域知识的数据来解决预测编码音频质量的问题。我们提出了一个可学习的神经网络，名为InSE-NET，具有Inception和Squeeze-and-Excitation模块的骨干，以48 kHz采样率评估编码音频的感知质量。我们证明了合成数据增强能够增强预测。我们提出的方法是侵入性的，即它需要未编码的参考信号的伽玛频谱图。除了与ViV 3相当的性能外，我们的方法还提供了对更高比特率的更鲁棒的预测。
英文摘要：
Automatic coded audio quality assessment is an important task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen codecs, bitrates, content-types, and a lack of flexibility of existing approaches. One of the typical human-perception-related metrics, ViSQOL v3 (ViV3), has been proven to provide a high correlation to the quality scores rated by humans. In this study, we take steps to tackle problems of predicting coded audio quality by completely utilizing programmatically generated data that is informed with expert domain knowledge. We propose a learnable neural network, entitled InSE-NET, with a backbone of Inception and Squeeze-and-Excitation modules to assess the perceived quality of coded audio at a 48kHz sample rate. We demonstrate that synthetic data augmentation is capable of enhancing the prediction. Our proposed method is intrusive, i.e. it requires Gammatone spectrograms of unencoded reference signals. Besides a comparable performance to ViV3, our approach provides a more robust prediction towards higher bitrates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
