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

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.

6 pages, 3 figures, accepted and presented at ACM MMSys22, June, 2022, Athlone, Ireland

References in corpus (6)

• Array Programming with NumPy
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets
• NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
• Can we still use PEAQ? A Performance Analysis of the ITU Standard for the Objective Assessment of Perceived Audio Quality
• More for Less: Non-Intrusive Speech Quality Assessment with Limited Annotations