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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.
Audio Similarity is Unreliable as a Proxy for Audio Quality
Many audio processing tasks require perceptual assessment.
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.
Learning from Shader Program Traces
We investigate this learning task for a variety of applications: our model can learn to predict a low-noise output image from shader programs that exhibit sampling noise; this model can also learn from a simplified shader program that approximates the reference solution with less computation, as well as learn the output of postprocessing filters like defocus blur and edge-aware sharpening.
HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks
Real-world audio recordings are often degraded by factors such as noise, reverberation, and equalization distortion.
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.
Text-based Editing of Talking-head Video
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
Perceptually-motivated Environment-specific Speech Enhancement
This paper introduces a deep learning approach to enhance speech recordings made in a specific environment.
PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup
This paper introduces an automatic method for editing a portrait photo so that the subject appears to be wearing makeup in the style of another person in a reference photo.
High-Precision Localization Using Ground Texture
We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside.
Finding Distractors In Images
We propose a new computer vision task we call "distractor prediction."
TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow.
