Papers Audio Quality Assessment
“Audio Quality Assessment” 태그가 달린 논문 18편 · 필터 해제
Robust Generative Audio Quality Assessment: Disentangling Quality from Spurious Correlations
The rapid proliferation of AI-Generated Content (AIGC) has necessitated robust metrics for perceptual quality assessment. However, automatic Mean Opinion Score (MOS) prediction models are often compromised by data scarci…
Audio Quality AssessmentImproving Perceptual Audio Aesthetic Assessment via Triplet Loss and Self-Supervised Embeddings
We present a system for automatic multi-axis perceptual quality prediction of generative audio, developed for Track 2 of the AudioMOS Challenge 2025. The task is to predict four Audio Aesthetic Scores--Production Quality…
Audio Quality AssessmentMMMOS: Multi-domain Multi-axis Audio Quality Assessment
Accurate audio quality estimation is essential for developing and evaluating audio generation, retrieval, and enhancement systems. Existing non-intrusive assessment models predict a single Mean Opinion Score (MOS) for sp…
Audio Quality AssessmentAudio GenerationTowards Improved Objective Perceptual Audio Quality Assessment -- Part 1: A Novel Data-Driven Cognitive Model
Efficient audio quality assessment is vital for streamlining audio codec development. Objective assessment tools have been developed over time to algorithmically predict quality ratings from subjective assessments, the g…
Audio Quality AssessmentPredictionRF-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 lever…
Audio Quality AssessmentTransfer LearningODAQ: Open Dataset of Audio Quality - Benchmark on GitHub
ODAQ is a dataset addressing the scarcity of openly available collections of audio signals accompanied by corresponding subjective scores of perceived quality. ODAQ contains 240 audio samples accompanied by correspond…
Audio Quality AssessmentBenchmarkingPAM: Prompting Audio-Language Models for Audio Quality Assessment
While audio quality is a key performance metric for various audio processing tasks, including generative modeling, its objective measurement remains a challenge. Audio-Language Models (ALMs) are pre-trained on audio-text…
Audio Quality AssessmentMusic GenerationText-to-Music Generationtext-to-speech+1HAAQI-Net: A Non-intrusive Neural Music Audio Quality Assessment Model for Hearing Aids
This paper introduces HAAQI-Net, a non-intrusive deep learning-based music audio quality assessment model for hearing aid users. Unlike traditional methods like the Hearing Aid Audio Quality Index (HAAQI) that require in…
Audio Quality AssessmentAudio Signal ProcessingComputational EfficiencyKnowledge Distillation+1ODAQ: Open Dataset of Audio Quality
Research into the prediction and analysis of perceived audio quality is hampered by the scarcity of openly available datasets of audio signals accompanied by corresponding subjective quality scores. To address this probl…
Audio Quality AssessmentDiversityMusic Quality AssessmentUncertainty as a Predictor: Leveraging Self-Supervised Learning for Zero-Shot MOS Prediction
Predicting audio quality in voice synthesis and conversion systems is a critical yet challenging task, especially when traditional methods like Mean Opinion Scores (MOS) are cumbersome to collect at scale. This paper add…
Audio Quality AssessmentSelf-Supervised LearningA Data-driven Cognitive Salience Model for Objective Perceptual Audio Quality Assessment
Objective audio quality measurement systems often use perceptual models to predict the subjective quality scores of processed signals, as reported in listening tests. Most systems map different metrics of perceived degra…
Audio Quality AssessmentSAQAM: Spatial Audio Quality Assessment Metric
Audio quality assessment is critical for assessing the perceptual realism of sounds. However, the time and expense of obtaining ''gold standard'' human judgments limit the availability of such data. For AR&VR, good perce…
Audio Quality AssessmentMulti-Task LearningSpeech EnhancementTripletInSE-NET: A Perceptually Coded Audio Quality Model based on CNN
Automatic coded audio quality assessment is an important task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen codecs, bitrates, content-types, and a lack of flexibility of e…
Audio Quality AssessmentData AugmentationPerceiving Music Quality with GANs
Several methods have been developed to assess the perceptual quality of audio under transforms like lossy compression. However, they require paired reference signals of the unaltered content, limiting their use in applic…
Audio GenerationAudio Quality AssessmentMusic GenerationStyle TransferExploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models
Many applications of speech technology require more and more audio data. Automatic assessment of the quality of the collected recordings is important to ensure they meet the requirements of the related applications. Howe…
Audio Quality AssessmentA novel fuzzy logic-based metric for audio quality assessment: Objective audio quality assessment
ITU-R BS.1387 states a method for objective assessment of perceived audio quality. This Recommendation, known also as PEAQ (Perceptual Evaluation of Audio Quality) is based on a psychoacoustic model of the human ear and …
Audio Quality AssessmentNon-intrusive speech quality assessment using neural networks
Estimating the perceived quality of an audio signal is critical for many multimedia and audio processing systems. Providers strive to offer optimal and reliable services in order to increase the user quality of experienc…
Audio Quality AssessmentP.862.2 : Wideband extension to Recommendation P.862 for the assessment of wideband telephone networks and speech codecs
ITU-T Recommendation P.862.2 describes a simple extension to the perceptual evaluation of listening speech quality (PESQ) algorithm defined in ITU-T Recommendation P.862. It allows ITU-T Recommendation P.862 to be appl…
Audio Quality Assessment