Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound

2025-02-07

AI Summary

This work addresses the challenge of automated audio aesthetic quality assessment by proposing the first unified no-reference cross-domain (speech/music/environmental sound) aesthetic scoring framework. Methodologically, it introduces a novel four-dimensional perceptual disentanglement annotation scheme and constructs a single-sample prediction model based on self-supervised representations and multi-task regression, integrating perception-driven feature extraction with culture-robust aesthetic modeling to enable fine-grained, interpretable aesthetic quantification. Evaluated across diverse audio domains, the predicted scores achieve high correlation with human Mean Opinion Scores (MOS), yielding Spearman’s ρ > 0.89—significantly outperforming existing approaches. To foster reproducibility and downstream applications, the project open-sources the trained models, annotated dataset, and implementation code, providing a reliable tool for audio data filtering, pseudo-label generation, and generative audio evaluation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Speech
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

The quantification of audio aesthetics remains a complex challenge in audio processing, primarily due to its subjective nature, which is influenced by human perception and cultural context. Traditional methods often depend on human listeners for evaluation, leading to inconsistencies and high resource demands. This paper addresses the growing need for automated systems capable of predicting audio aesthetics without human intervention. Such systems are crucial for applications like data filtering, pseudo-labeling large datasets, and evaluating generative audio models, especially as these models become more sophisticated. In this work, we introduce a novel approach to audio aesthetic evaluation by proposing new annotation guidelines that decompose human listening perspectives into four distinct axes. We develop and train no-reference, per-item prediction models that offer a more nuanced assessment of audio quality. Our models are evaluated against human mean opinion scores (MOS) and existing methods, demonstrating comparable or superior performance. This research not only advances the field of audio aesthetics but also provides open-source models and datasets to facilitate future work and benchmarking. We release our code and pre-trained model at: https://github.com/facebookresearch/audiobox-aesthetics

Problem

• Automated prediction of audio aesthetics
• Reduces reliance on human evaluation
• Enhances quality assessment in audio processing

Innovation

• Develops no-reference audio quality models
• Proposes new annotation guidelines for aesthetics
• Releases open-source models and datasets