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Found 12 papers, 5 papers with code
CORN: Co-Trained Full- And No-Reference Speech Quality Assessment
In contrast, no-reference (NR) metrics evaluate a recording without relying on a reference.
TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio
To enable this, a variety of metrics to measure quality and intelligibility under different assumptions have been developed.
Audio Similarity is Unreliable as a Proxy for Audio Quality
Many audio processing tasks require perceptual assessment.
SAQAM: Spatial Audio Quality Assessment Metric
Audio quality assessment is critical for assessing the perceptual realism of sounds.
Speech Quality Assessment through MOS using Non-Matching References
Human judgments obtained through Mean Opinion Scores (MOS) are the most reliable way to assess the quality of speech signals.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References
We show that neural networks trained using our framework produce scores that correlate well with subjective mean opinion scores (MOS) and are also competitive to methods such as DNSMOS, which explicitly relies on MOS from humans for training networks.
DPLM: A Deep Perceptual Spatial-Audio Localization Metric
Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality.
CDPAM: Contrastive learning for perceptual audio similarity
The DPAM approach of Manocha et al. learns a full-reference metric trained directly on human judgments, and thus correlates well with human perception.
A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
Assessment of many audio processing tasks relies on subjective evaluation which is time-consuming and expensive.
Tumor Classification and Segmentation of MR Brain Images
The diagnosis and segmentation of tumors using any medical diagnostic tool can be challenging due to the varying nature of this pathology.
Automated Tumor Segmentation and Brain Mapping for the Tumor Area
Magnetic Resonance Imaging (MRI) is an important diagnostic tool for precise detection of various pathologies.
