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Audio quality assessment has been widely researched in the signal processingarea. Full-reference objective metrics (e.g., POLQA, ViSQOL) have beendeveloped to estimate the audio quality relying only on human ratingexperiments. To evaluate the audio quality of novel audio processingtechniques, researchers constantly need to compare objective quality metrics.Testing different implementations of the same metric and evaluating newdatasets are fundamental and ongoing iterative activities. In this paper, wepresent AQP - an open-source, node-based, light-weight Python pipeline foraudio quality assessment. AQP allows researchers to test and compare objectivequality metrics helping to improve robustness, reproducibility and developmentspeed. We introduce the platform, explain the motivations, and illustrate withexamples how, using AQP, objective quality metrics can be (i) compared andbenchmarked; (ii) prototyped and adapted in a modular fashion; (iii) visualisedand checked for errors. The code has been shared on GitHub to encourageadoption and contributions from the community.

AQP: An Open Modular Python Platform for Objective Speech and Audio Quality Metrics