Audio quality assessment has been widely researched in the signal processing area. Full-reference objective metrics (e.g., POLQA, ViSQOL) have been developed to estimate the audio quality relying only on human rating experiments. To evaluate the audio quality of novel audio processing techniques, researchers constantly need to compare objective quality metrics. Testing different implementations of the same metric and evaluating new datasets are fundamental and ongoing iterative activities. In this paper, we present AQP - an open-source, node-based, light-weight Python pipeline for audio quality assessment. AQP allows researchers to test and compare objective quality metrics helping to improve robustness, reproducibility and development speed. We introduce the platform, explain the motivations, and illustrate with examples how, using AQP, objective quality metrics can be (i) compared and benchmarked; (ii) prototyped and adapted in a modular fashion; (iii) visualised and checked for errors. The code has been shared on GitHub to encourage adoption and contributions from the community.
翻译:对信号处理领域的音质评估进行了广泛的研究,并制定了全面参考目标指标(例如POLQA、VisQOL),以评估仅依靠人类评级试验的音质质量;为了评估新音频处理技术的音质质量,研究人员经常需要比较客观质量指标;测试同一指标的不同执行情况和评价新的数据集是基本和持续进行的迭接活动;在本文件中,我们介绍了AQP-开放源码、节点基、轻量Python管道,用于音质评估;AQP允许研究人员测试和比较客观质量指标,帮助提高稳健性、可复制性和发展速度;我们介绍了平台,解释动机,并举例说明如何利用AQP(一)比较和基准;(二)以模块方式进行原型和调整;(三)可视化和检查错误;在GitHub分享了代码,以鼓励社区的采纳和贡献。