5 citations · 14 across the 13 of their papers we have counts for
15 papers
Exploring Multimodal Turn-Taking Cues in Face-to-Face Conversation using Voice Activity Projection
Willem Berner, Julio Cesar Cavalcanti, Kalle Åström +1
Turn-taking is a fundamental component of spoken interaction, and while humans naturally rely on both verbal and non-verbal signals, dialogue systems usually depend on audio cues a…
PixCuboid: Room Layout Estimation from Multi-view Featuremetric Alignment
Gustav Hanning, Kalle Åström, Viktor Larsson
Coarse room layout estimation provides important geometric cues for many downstream tasks. Current state-of-the-art methods are predominantly based on single views and often assume…
Converting T1-weighted MRI from 3T to 7T quality using deep learning
Malo Gicquel, Ruoyi Zhao, Anika Wuestefeld +12
Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3…
Single-Source Localization as an Eigenvalue Problem
Martin Larsson, Viktor Larsson, Kalle Åström +1
This paper introduces a novel method for solving the single-source localization problem, specifically addressing the case of trilateration. We formulate the problem as a weighted l…
SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio
Erik Tegler, Magnus Oskarsson, Kalle Åström
Time delay estimation or Time-Difference-Of-Arrival estimates is a critical component for multiple localization applications such as multilateration, direction of arrival, and self…
The Impact of Semi-Supervised Learning on Line Segment Detection
Johanna Engman, Karl Åström, Magnus Oskarsson
In this paper we present a method for line segment detection in images, based on a semi-supervised framework. Leveraging the use of a consistency loss based on differently augmente…
Learning Multi-Target TDOA Features for Sound Event Localization and Detection
Axel Berg, Johanna Engman, Jens Gulin +2
Sound event localization and detection (SELD) systems using audio recordings from a microphone array rely on spatial cues for determining the location of sound events. As a consequ…
wav2pos: Sound Source Localization using Masked Autoencoders
Axel Berg, Jens Gulin, Mark O'Connor +3
We present a novel approach to the 3D sound source localization task for distributed ad-hoc microphone arrays by formulating it as a set-to-set regression problem. By training a mu…
The LuViRA Dataset: Synchronized Vision, Radio, and Audio Sensors for Indoor Localization
Ilayda Yaman, Guoda Tian, Martin Larsson +11
We present a synchronized multisensory dataset for accurate and robust indoor localization: the Lund University Vision, Radio, and Audio (LuViRA) Dataset. The dataset includes colo…
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue
Aleksis Pirinen, Anton Samuelsson, John Backsund +1
Climate-induced disasters are and will continue to be on the rise, and thus search-and-rescue (SAR) operations, where the task is to localize and assist one or several people who a…
Multiple Offsets Multilateration: a new paradigm for sensor network calibration with unsynchronized reference nodes
Luca Ferranti, Kalle Åström, Magnus Oskarsson +2
Positioning using wave signal measurements is used in several applications, such as GPS systems, structure from sound and Wifi based positioning. Mathematically, such problems requ…
Homotopy Continuation for Sensor Networks Self-Calibration
Luca Ferranti, Kalle Åström, Magnus Oskarsson +2
Given a sensor network, TDOA self-calibration aims at simultaneously estimating the positions of receivers and transmitters, and transmitters time offsets. This can be formulated a…
Generic Merging of Structure from Motion Maps with a Low Memory Footprint
Gabrielle Flood, David Gillsjö, Patrik Persson +2
With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged.…
Trust Your IMU: Consequences of Ignoring the IMU Drift
Marcus Valtonen Örnhag, Patrik Persson, Mårten Wadenbäck +2
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us t…
In Depth Bayesian Semantic Scene Completion
David Gillsjö, Kalle Åström
This work studies Semantic Scene Completion which aims to predict a 3D semantic segmentation of our surroundings, even though some areas are occluded. For this we construct a Bayes…