Search Results for author:
Found 34 papers, 8 papers with code
Multi-Channel CNN-based Object Detection for Enhanced Situation Awareness
In this study, we propose a novel detection algorithm for military objects by fusing multi-channel CNNs.
Efficient Traffic-Sign Recognition with Scale-aware CNN
The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images.
IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation
Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights.
Single-Channel Speech Separation with Auxiliary Speaker Embeddings
We present a novel source separation model to decompose asingle-channel speech signal into two speech segments belonging to two different speakers.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System
N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression.
Sound Audio and Speech Processing
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)
We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
On designing finite time iterative learning control based on steady state frequency response
Iterative Learning Control (ILC) is useful in spacecraft application for repeated high precision scanning maneuvers.
Modifying and optimizing the inverse of the frequency response circulant matrix as an iterative learning control compensator
The purpose of this paper is to create a method of designing ILC compensators based on steady state frequency response, and have the ILC converge to zero error in spite of transients and bandwidth.
Multistage linguistic conditioning of convolutional layers for speech emotion recognition
In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER).
Semi-supervised Multi-task Learning for Semantics and Depth
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning
We demonstrate, both theoretically and empirically, the merits of our proposed redundancy model in improving the robustness of DGD against asynchronous and Byzantine agents, and their extensions to distributed stochastic gradient descent (D-SGD) for robust distributed machine learning with asynchronous and Byzantine agents.
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration.
Audio Self-supervised Learning: A Survey
Inspired by the humans' cognitive ability to generalise knowledge and skills, Self-Supervised Learning (SSL) targets at discovering general representations from large-scale data without requiring human annotations, which is an expensive and time consuming task.
A Temporal-oriented Broadcast ResNet for COVID-19 Detection
Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.
An adaptive graph learning method for automated molecular interactions and properties predictions
In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery.
Ranked #1 on
Drug Discovery
on ToxCast (Toxicity Forecaster)
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
TLP: A Deep Learning-based Cost Model for Tensor Program Tuning
Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives.
Impact of Redundancy on Resilience in Distributed Optimization and Learning
In particular, we introduce the notion of $(f, r; \epsilon)$-resilience to characterize how well the true solution is approximated in the presence of up to $f$ Byzantine faulty agents, and up to $r$ slow agents (or stragglers) -- smaller $\epsilon$ represents a better approximation.
Towards Selection of Text-to-speech Data to Augment ASR Training
This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration
This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows.
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning.
A Method to Speed Up Convergence of Iterative Learning Control for High Precision Repetitive Motions
Iterative Learning Control (ILC) records previous run tracking error, adjusts the next run command, aiming for zero tracking error in the real world, not our model of the world.
Startup Acquisitions: Acquihires and Talent Hoarding
We study how competitive forces may drive firms to inefficiently acquire startup talent.
Enhancing Psychological Counseling with Large Language Model: A Multifaceted Decision-Support System for Non-Professionals
In the contemporary landscape of social media, an alarming number of users express negative emotions, some of which manifest as strong suicidal intentions.
FishMOT: A Simple and Effective Method for Fish Tracking Based on IoU Matching
Wherein, a basic module performs target association based on IoU of detection boxes between successive frames to deal with morphological change of fish; an interaction module combines IoU of detection boxes and IoU of fish entity to handle occlusions; a refind module use spatio-temporal information uses spatio-temporal information to overcome the tracking failure resulting from the missed detection by the detector under complex environment.
Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media
In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification.
Vignat: Vulnerability identification by learning code semantics via graph attention networks
Vulnerability identification is crucial to protect software systems from attacks for cyber-security.
Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs.
SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10.
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
Specifically, UniTrans first craft a series of test cases for target programs with the assistance of source programs.
