RF-GML: Reference-Free Generative Machine Listener
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows
Robust-Multi-Task Gradient Boosting
PSAT: Pediatric Segmentation Approaches via Adult Augmentations and Transfer Learning
A Survey on Prompt Tuning
DS@GT at CheckThat! 2025: Detecting Subjectivity via Transfer-Learning and Corrective Data Augmentation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.