Speech enhancement using artificial neural networks aims to remove noise from noisy speech signals while preserving the speech content. However, speech enhancement networks often introduce distortions to the speech signal, referred to as artifacts, which can degrade audio quality. In this work, we propose a post-processing neural network designed to mitigate artifacts introduced by speech enhancement models. Inspired by the analogy of making a `Putt' after an `Approach' in golf, we name our model PuttNet. We demonstrate that alternating between a speech enhancement model and the proposed Putt model leads to improved speech quality, as measured by perceptual quality scores (PESQ), objective intelligibility (STOI), and background noise intrusiveness (CBAK) scores. Furthermore, we illustrate with graphical analysis why this alternating Approach outperforms repeated application of either model alone.

The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks to design such compact and high-quality representations. However, the ability to optimize tensor-based representations, and in particular, the highly compact tensor train representation, is still lacking. This has prevented practitioners from deploying the full potential of tensor networks for visual data. To this end, we propose 'Prolongation Upsampling Tensor Train (PuTT)', a novel method for learning tensor train representations in a coarse-to-fine manner. Our method involves the prolonging or `upsampling' of a learned tensor train representation, creating a sequence of 'coarse-to-fine' tensor trains that are incrementally refined. We evaluate our representation along three axes: (1). compression, (2). denoising capability, and (3). image completion capability. To assess these axes, we consider the tasks of image fitting, 3D fitting, and novel view synthesis, where our method shows an improved performance compared to state-of-the-art tensor-based methods. For full results see our project webpage: https://sebulo.github.io/PuTT_website/

From delivering food to your door, serving us coffee and even removing cancerous tumors, robots can already complete a range of impressive tasks. But now a robot has taken on the golf course, being able to navigate itself to a ball and even sink a putt. Thanks to a 3D camera, the impressive robot dubbed Golfi can find golf balls and wheel itself into place before taking a shot. The camera uses an algorithm to detect hard-coded objects, scan the area and find the ball. Golfi (pictured) was created by the Paderborn University in Germany.

A robot called Golfi is the first to be able to autonomously spot and travel to a golf ball anywhere on a green and sink a putt. Golf-playing robots have been developed before, but they have needed humans to set them up in front of a ball and program them to make the correct swing. The most famous is LDRIC, a robot that hit a lengthy hole-in-one at Arizona's TPC Scottsdale golf course in 2016. In contrast, Golfi, engineered by Annika Junker at Paderborn University in Germany and her colleagues, can find golf balls and wheel itself into place thanks to input from a 3D camera that looks down on a green from above. The camera scans the green and an algorithm then approximates the surface before simulating 3000 golf swings towards the hole from random points, taking into account factors such as the speed and weight of the ball and the friction of the green, which are described by physics-based equations.

A robot called Golfi is the first to be able to autonomously spot and travel to a golf ball anywhere on a green and sink a putt. Golf-playing robots have been developed before, but they have needed humans to set them up in front of a ball and program them to make the correct swing. The most famous is LDRIC, a robot that hit a lengthy hole-in-one at Arizona's TPC Scottsdale golf course in 2016. In contrast, Golfi, engineered by Annika Junker at Paderborn University in Germany and her colleagues, can find golf balls and wheel itself into place thanks to input from a 3D camera that looks down on a green from above. The camera scans the green and an algorithm then approximates the surface before simulating 3000 golf swings towards the hole from random points, taking into account factors such as the speed and weight of the ball and the friction of the green, which are described by physics-based equations.

Robots that can whack a golf ball down a fairway aren't exactly new, but building one that can play the nuanced short game is a more complex problem. Researchers at Paderborn University in Germany have done just that with Golfi, a machine that uses a neural network to figure out how to line up a putt and how hard to hit the ball to get it into the hole from anywhere on the green. The robot takes a snapshot of the green with a Microsoft Kinect 3D camera and it simulates thousands of random shots taken from different positions. It takes factors like the turf's rolling resistance, the ball's weight and the starting velocity into account. Paderborn doctoral student Annika Junker told IEEE Research that training Golfi on simulated golf shots takes five minutes, compared with 30-40 hours were the team to feed data from real-life shots into the system.