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

TL;DR

AQP is an open-source Python platform that simplifies the testing, comparison, and development of objective speech and audio quality metrics, enhancing reproducibility and community collaboration.

Contribution

It introduces a modular, node-based Python pipeline for audio quality assessment, facilitating benchmarking, prototyping, and visualization of metrics.

Findings

Enables easy comparison of different audio quality metrics

Supports rapid prototyping and adaptation of metrics

Promotes reproducibility and community contributions

Abstract

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.