  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.
