RF-GML:无参考生成机器监听器

Arijit Biswas, Guanxin Jiang
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摘要

本文介绍了一种名为 RF-Generative Machine Listener(RF-GML)的新型无参考(RF)音频质量度量,旨在评估 48 kHz 采样率下的编码单声道、立体声和双声道音频。RF-GML 利用了最先进的全参考(FR)生成式机器监听器(GML)的迁移学习功能,只做了最小的架构修改。所谓 "生成",是指该模型能够生成任意数量的模拟收听分数。与现有的 RF 模型不同,RF-GML 可以准确预测不同内容类型和编解码器的主观质量分数。广泛的评估证明了它在为未编码音频评分和区分不同程度的编码人工痕迹方面的优越性。RF-GML 的性能和通用性使其成为各种应用中编码音频质量评估和监控的重要工具,而且无需参考信号。
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RF-GML: Reference-Free Generative Machine Listener
This paper introduces a novel reference-free (RF) audio quality metric called the RF-Generative Machine Listener (RF-GML), designed to evaluate coded mono, stereo, and binaural audio at a 48 kHz sample rate. RF-GML leverages transfer learning from a state-of-the-art full-reference (FR) Generative Machine Listener (GML) with minimal architectural modifications. The term "generative" refers to the model's ability to generate an arbitrary number of simulated listening scores. Unlike existing RF models, RF-GML accurately predicts subjective quality scores across diverse content types and codecs. Extensive evaluations demonstrate its superiority in rating unencoded audio and distinguishing different levels of coding artifacts. RF-GML's performance and versatility make it a valuable tool for coded audio quality assessment and monitoring in various applications, all without the need for a reference signal.
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