Speaker diarization has gained considerable attention within speech
proc...
Automatic Mean Opinion Score (MOS) prediction is crucial to evaluate the...
Massive emerging applications are driving demand for the ubiquitous
depl...
Understanding the life cycle of the machine learning (ML) model is an
in...
Federated learning is a decentralized and privacy-preserving technique t...
Disentangling uncorrelated information in speech utterances is a crucial...
Gaussian process regression (GPR) is a non-parametric model that has bee...
Speaker diarization(SD) is a classic task in speech processing and is cr...
Effective fusion of multi-scale features is crucial for improving speake...
State-of-art NPUs are typically architected as a self-contained sub-syst...
Time delay neural network (TDNN) has been proven to be efficient for spe...
Recent work proposed the UCTMAXSAT algorithm to address Maximum
Satisfia...
U-shaped networks are widely used in various medical image tasks, such a...
Training robust speaker verification systems without speaker labels has ...
Robotic arms are widely used in automatic industries. However, with wide...
Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible
...
Gait recognition, which can realize long-distance and contactless
identi...
Large-scale Protein Language Models (PLMs) have improved performance in
...
Relevant recommendation is a special recommendation scenario which provi...
Temporal convolutions have been the paradigm of choice in action
segment...
X-ray photon correlation spectroscopy (XPCS) allows for the resolution o...
In this paper, we propose a novel prosody disentangle method for prosodi...
Nearest neighbor search plays a fundamental role in many disciplines suc...
Full-band speech enhancement based on deep neural networks is still
chal...
The decoupling-style concept begins to ignite in the speech enhancement ...
Power estimation is the basis of many hardware optimization strategies.
...
This paper focuses on developing energy-efficient online data processing...
Curriculum learning begins to thrive in the speech enhancement area, whi...
For the lack of adequate paired noisy-clean speech corpus in many real
s...
Cycle-consistent generative adversarial networks (CycleGAN) have shown t...
Non-parallel training is a difficult but essential task for DNN-based sp...
In this paper, we propose a novel image process scheme called class-base...
Mobile edge computing (MEC) has recently become a prevailing technique t...
Due to the flexibility in modelling data heterogeneity, heterogeneous
in...
Blind image deblurring is an important yet very challenging problem in
l...
AlphaZero has achieved impressive performance in deep reinforcement lear...
The advent of distributed energy resources (DERs), such as distributed
r...
Mobile edge computing (MEC) is a promising paradigm to accommodate the
i...
k-nearest neighbor graph is a key data structure in many disciplines suc...
Unsupervised domain adaptation (UDA) typically carries out knowledge tra...
In this paper, we consider a multi-user mobile-edge computing (MEC) netw...
Federated Learning (FL) is an emerging paradigm through which decentrali...
Intelligent reflecting surface (IRS) is an emerging technology to enhanc...
In recent years, there are a large number of recommendation algorithms
p...
Opportunistic computation offloading is an effective method to improve t...
Recently, conversational recommender system (CRS) has become an emerging...
Recently, significant progress has been made in sequential recommendatio...
With the memory-resource-limited constraints, class-incremental learning...
As an important and challenging problem, multi-domain learning (MDL)
typ...
Intelligent reflecting surface (IRS) can effectively enhance the energy ...