Search Results for author:
Found 122 papers, 58 papers with code
Graph-less Collaborative Filtering
Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning.
DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet.
Disentangled Graph Social Recommendation
In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections.
Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance.
Heterogeneous Graph Contrastive Learning for Recommendation
In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views.
Multi-Behavior Graph Neural Networks for Recommender System
Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.
Million-scale Object Detection with Large Vision Model
Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system.
Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion
The experimental results of our method are superior to those of the state-of-the-art unsupervised methods by a large margin.
Deep Neural Mel-Subband Beamformer for In-car Speech Separation
While current deep learning (DL)-based beamforming techniques have been proved effective in speech separation, they are often designed to process narrow-band (NB) frequencies independently which results in higher computational costs and inference times, making them unsuitable for real-world use.
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition
Specifically, TIM-Net first employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally, fuses multiple time scale features for better adaptation to the emotional variation.
Ranked #1 on
Speech Emotion Recognition
on EMOVO
GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition
In this paper, we propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component with a multi-scale receptive field.
Prompt-driven efficient Open-set Semi-supervised Learning
In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters.
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
Localized Sparse Incomplete Multi-view Clustering
Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation.
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognition
For the single-corpus task, the combination of Convolution-Pooling and Attention CapsNet module CPAC) is designed by embedding the self-attention mechanism to the CapsNet, guiding the module to focus on the important features that can be fed into different capsules.
Self-Supervised Low-Light Image Enhancement Using Discrepant Untrained Network Priors
The enhancement is done by jointly optimizing the Retinex decomposition and the illumination adjustment.
Multi-Behavior Sequential Recommendation with Temporal Graph Transformer
The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.
Deniable Steganography
With a stego media discovered, the adversary could find out the sender or receiver and coerce them to disclose the secret message, which we name as coercive attack in this paper.
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement
Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when the loudspeaker plays back.
Hypergraph Contrastive Collaborative Filtering
Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.
Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.
Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification
The proposed VABPP method is the first time that the view-aware-based method is used as a post-processing method in the field of vehicle re-identification.
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers
In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting.
Fine-Grained Object Classification via Self-Supervised Pose Alignment
Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods.
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.
Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks.
Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.
Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning
Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis
To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN.
Specificity-Preserving Federated Learning for MR Image Reconstruction
The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.
SwinTrack: A Simple and Strong Baseline for Transformer Tracking
The potential of Transformer in representation learning remains under-explored.
Ranked #4 on
Visual Object Tracking
on TrackingNet
Encoding Spatial Distribution of Convolutional Features for Texture Representation
Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation.
Joint Neural AEC and Beamforming with Double-Talk Detection
We train the proposed model in an end-to-end approach to eliminate background noise and echoes from far-end audio devices, which include nonlinear distortions.
Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
Social Recommendation with Self-Supervised Metagraph Informax Network
To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.
Graph Meta Network for Multi-Behavior Recommendation
Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.
Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.
Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network
The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.
Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks
Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image.
Heterogeneous relational message passing networks for molecular dynamics simulations
With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of computational sciences underpinning physics, material science, chemistry, and biology.
Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing
However, it is noted that ASM-based SIDM degrades its performance in dehazing real world hazy images due to the limited modelling ability of ASM where the atmospheric light factor (ALF) and the angular scattering coefficient (ASC) are assumed as constants for one image.
Multi-Modal Transformer for Accelerated MR Imaging
To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification
Since human-labeled samples are free for the target set, unsupervised person re-identification (Re-ID) has attracted much attention in recent years, by additionally exploiting the source set.
Deep Texture Recognition via Exploiting Cross-Layer Statistical Self-Similarity
In recent years, convolutional neural networks (CNNs) have become a prominent tool for texture recognition.
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.
Ranked #9 on
Image Super-Resolution
on IXI
Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network
In this work, we propose a multi-stage integration network (i. e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR.
DONet: Dual-Octave Network for Fast MR Image Reconstruction
In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction.
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation
The spatial self-attention module is designed to attend on the cross-channel correlation in the covariance matrices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction
We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction.
MetricNet: Towards Improved Modeling For Non-Intrusive Speech Quality Assessment
The objective speech quality assessment is usually conducted by comparing received speech signal with its clean reference, while human beings are capable of evaluating the speech quality without any reference, such as in the mean opinion score (MOS) tests.
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments.
Asymmetric CNN for image super-resolution
In this paper, we propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a memory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution.
Distributed Newton Optimization with Maximized Convergence Rate
As the second step towards our goal we complement the proposed method with a fully distributed method for estimating the optimal step size that maximizes convergence speed.
Distributed Optimization
Optimization and Control
MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently.
Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient
Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications.
Mesoscale and Nanoscale Physics
FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation
However, for images taken in real-world, the illumination is not uniformly distributed over whole image which brings model mismatch and possibly results in color shift of the deep models using ASM.
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
Molecular dynamics is a powerful simulation tool to explore material properties.
Hypergraph Neural Networks for Hypergraph Matching
Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph.
Ranked #7 on
Graph Matching
on Willow Object Class
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation
To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real images to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator's feature space.
Detection of magnetic gap in the topological surface states of MnBi2Te4
An essential ingredient to realize these quantum states is the magnetic gap in the topological surface states induced by the out-of-plane ferromagnetism on the surface of MnBi2Te4.
Materials Science
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Vehicle Re-identification Based on Dual Distance Center Loss
Moreover, by designing a Euclidean distance threshold between all center pairs, which not only strengthens the inter-class separability of center loss, but also makes the center loss (or DDCL) works well without the combination of softmax loss.
Structural Disorder Induced Second-order Topological Insulators in Three Dimensions
Here we predict the existence of a secondorder topological insulating phase in an amorphous system without any crystalline symmetry.
Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks
Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization
The advantages of D-ASR over existing methods are threefold: (1) it provides explicit speaker locations, (2) it improves the explainability factor, and (3) it achieves better ASR performance as the process is more streamlined.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources.
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Speech separation algorithms are often used to separate the target speech from other interfering sources.
Recurrent Exposure Generation for Low-Light Face Detection
REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
Designing and Training of A Dual CNN for Image Denoising
The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network.
Lightweight image super-resolution with enhanced CNN
Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image.
Deep Bilateral Retinex for Low-Light Image Enhancement
Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.
Neural Spatio-Temporal Beamformer for Target Speech Separation
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmful for the automatic speech recognition (ASR).
Audio and Speech Processing Sound
Pathwise unique solutions and stochastic averaging for mixed stochastic partial differential equations driven by fractional Brownian motion and Brownian motion
This paper is devoted to a system of stochastic partial differential equations (SPDEs) that have a slow component driven by fractional Brownian motion (fBm) with the Hurst parameter $H >1/2$ and a fast component driven by fast-varying diffusion.
Probability Dynamical Systems 60G22, 60H05, 60H15, 34C29
Multi-modal Multi-channel Target Speech Separation
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers.
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Hand-crafted spatial features (e. g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods.
Self-supervised learning for audio-visual speaker diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
Deep Learning on Image Denoising: An overview
However, there are substantial differences in the various types of deep learning methods dealing with image denoising.
A Unified Framework for Speech Separation
The initial solutions introduced for deep learning based speech separation analyzed the speech signals into time-frequency domain with STFT; and then encoded mixed signals were fed into a deep neural network based separator.
Adaptive GNN for Image Analysis and Editing
In practical verification, we design a new regularization structure with guided feature to produce GNN-based filtering and propagation diffusion to tackle the ill-posed inverse problems of quotient image analysis (QIA), which recovers the reflectance ratio as a signature for image analysis or adjustment.
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments.
Audio and Speech Processing Sound Signal Processing
Image denoising using deep CNN with batch renormalization
In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet).
Dedge-AGMNet:an effective stereo matching network optimized by depth edge auxiliary task
To improve the performance in ill-posed regions, this paper proposes an atrous granular multi-scale network based on depth edge subnetwork(Dedge-AGMNet).
Coupled-Projection Residual Network for MRI Super-Resolution
The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer.
Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available.
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results.
Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data
To overcome this problem, this study developed a new PCA method, which is named the Supervised Discriminative Sparse PCA (SDSPCA).
A comprehensive study of speech separation: spectrogram vs waveform separation
We study the speech separation problem for far-field data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information.
End-to-End Multi-Channel Speech Separation
This paper extended the previous approach and proposed a new end-to-end model for multi-channel speech separation.
Time Domain Audio Visual Speech Separation
Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement.
Audio and Speech Processing Sound
Image Cartoon-Texture Decomposition Using Isotropic Patch Recurrence
Aiming at separating the cartoon and texture layers from an image, cartoon-texture decomposition approaches resort to image priors to model cartoon and texture respectively.
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising.
Deep Learning for Image Denoising: A Survey
Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing.
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision.
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization
Clustering with incomplete views is a challenge in multi-view clustering.
Sound Event Detection and Time-Frequency Segmentation from Weakly Labelled Data
Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio clip.
Sound Audio and Speech Processing
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions.
A joint separation-classification model for sound event detection of weakly labelled data
First, we propose a separation mapping from the time-frequency (T-F) representation of an audio to the T-F segmentation masks of the audio events.
Sound Audio and Speech Processing
Audio Set classification with attention model: A probabilistic perspective
Then the classification of a bag is the expectation of the classification output of the instances in the bag with respect to the learned probability measure.
Sound Audio and Speech Processing
Large-scale weakly supervised audio classification using gated convolutional neural network
In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge.
Sound Audio and Speech Processing
Discriminative Block-Diagonal Representation Learning for Image Recognition
In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semi-supervised framework of low-rank representation.
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Specifically, we introduce class-specific auxiliary weights into the original MMD for exploiting the class prior probability on source and target domains, whose challenge lies in the fact that the class label in target domain is unavailable.
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
The inverse mapping of GANs'(Generative Adversarial Nets) generator has a great potential value. Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning. While the results are encouraging, the problem is highly challenging and the existing ways of training inverse models of GANs have many disadvantages, such as hard to train or poor performance. Due to these reasons, we propose a new approach based on using inverse generator ($IG$) model as encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to train the $IG$ model.
Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement
We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals.
Sound
Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging
Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge.
Sound
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
In this paper, we propose to use a convolutional neural network (CNN) to extract robust features from mel-filter banks (MFBs), spectrograms or even raw waveforms for audio tagging.
A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data
The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user.
Sound
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
For the unsupervised feature learning, we propose to use a symmetric or asymmetric deep de-noising auto-encoder (sDAE or aDAE) to generate new data-driven features from the Mel-Filter Banks (MFBs) features.
Hierarchical learning for DNN-based acoustic scene classification
In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework.
Lecture bilingue augment\'ee par des alignements multi-niveaux (Augmenting bilingual reading with alignment information)
Le travail qui a conduit {\`a} cette d{\'e}monstration combine des outils de traitement des langues multilingues, en particulier l{'}alignement automatique, avec des techniques de visualisation et d{'}interaction.
Fully DNN-based Multi-label regression for audio tagging
Compared with the conventional Gaussian Mixture Model (GMM) and support vector machine (SVM) methods, the proposed fully DNN-based method could well utilize the long-term temporal information with the whole chunk as the input.
Natural Scene Character Recognition Using Robust PCA and Sparse Representation
Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text.
Sparse Coding for Classification via Discrimination Ensemble
Discriminative sparse coding has emerged as a promising technique in image analysis and recognition, which couples the process of classifier training and the process of dictionary learning for improving the discriminability of sparse codes.
Novel elicitation and annotation schemes for sentential and sub-sentential alignments of bitexts
Resources for evaluating sentence-level and word-level alignment algorithms are unsatisfactory.
A survey of sparse representation: algorithms and applications
The main purpose of this article is to provide a comprehensive study and an updated review on sparse representation and to supply a guidance for researchers.
Removing Rain From a Single Image via Discriminative Sparse Coding
The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i. e. separate the rain layer and the de-rained image layer from an rainy image.
Lacunarity Analysis on Image Patterns for Texture Classification
Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including photometric changes and geometric changes.
