RF-GML: Reference-Free Generative Machine Listener

Arijit Biswas, Guanxin Jiang
{"title":"RF-GML: Reference-Free Generative Machine Listener","authors":"Arijit Biswas, Guanxin Jiang","doi":"arxiv-2409.10210","DOIUrl":null,"url":null,"abstract":"This paper introduces a novel reference-free (RF) audio quality metric called\nthe RF-Generative Machine Listener (RF-GML), designed to evaluate coded mono,\nstereo, and binaural audio at a 48 kHz sample rate. RF-GML leverages transfer\nlearning from a state-of-the-art full-reference (FR) Generative Machine\nListener (GML) with minimal architectural modifications. The term \"generative\"\nrefers to the model's ability to generate an arbitrary number of simulated\nlistening scores. Unlike existing RF models, RF-GML accurately predicts\nsubjective quality scores across diverse content types and codecs. Extensive\nevaluations demonstrate its superiority in rating unencoded audio and\ndistinguishing different levels of coding artifacts. RF-GML's performance and\nversatility make it a valuable tool for coded audio quality assessment and\nmonitoring in various applications, all without the need for a reference\nsignal.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Audio and Speech Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10210","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
RF-GML:无参考生成机器监听器
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Exploring an Inter-Pausal Unit (IPU) based Approach for Indic End-to-End TTS Systems Conformal Prediction for Manifold-based Source Localization with Gaussian Processes Insights into the Incorporation of Signal Information in Binaural Signal Matching with Wearable Microphone Arrays Dense-TSNet: Dense Connected Two-Stage Structure for Ultra-Lightweight Speech Enhancement Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1