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Audio Quality Assessment

Computational audio quality assessment aims to predict the quality of audio signals as perceived by expert listeners.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 15 papers

TitleStatusHype
Towards Improved Objective Perceptual Audio Quality Assessment -- Part 1: A Novel Data-Driven Cognitive Model—0
RF-GML: Reference-Free Generative Machine Listener—0
ODAQ: Open Dataset of Audio Quality - Benchmark on GitHubCode1
PAM: Prompting Audio-Language Models for Audio Quality AssessmentCode2
HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing AidsCode1
ODAQ: Open Dataset of Audio QualityCode1
Uncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction—0
A Data-driven Cognitive Salience Model for Objective Perceptual Audio Quality Assessment—0
SAQAM: Spatial Audio Quality Assessment Metric—0
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NMRPearson correlation coefficient (PCC)0.89—Unverified
2PEAQ-CSMPearson correlation coefficient (PCC)0.89—Unverified
32f-modelPearson correlation coefficient (PCC)0.87—Unverified
4PEAQ (ODG)Pearson correlation coefficient (PCC)0.87—Unverified
5ViSQOLAudioV3Pearson correlation coefficient (PCC)0.77—Unverified
6SMAQPearson correlation coefficient (PCC)0.77—Unverified
7PESQPearson correlation coefficient (PCC)0.74—Unverified
8SI-SDRPearson correlation coefficient (PCC)0.44—Unverified
9DNSMOS (OVRL)Pearson correlation coefficient (PCC)0.38—Unverified