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A Robust Audio-Visual Speech Enhancement Model
Most existing audio-visual speech enhancement (AVSE) methods work well in conditions with strong noise,however when applied to conditions with a medium SNR, serious performance degradations are often observed. These degradations can be partly attributed t
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Challenges And Perspectives In Neuromorphic-Based Visual Iot Systems And Networks
Neuromorphic sensors, a.k.a. dynamic vision sensors (DVS) or silicon retinas, do not capture full images (frames) at a fixed rate, but asynchronously capture spikes indicating changes of brightness in the scene, following the principles of biological visi
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$\Beta$-Nmf And Sparsity Promoting Regularizations For Complex Mixture Unmixing. Application To 2D Hsqc Nmr.
In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a b
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Low-Complexity Compressed Alignment-Aided Compressive Analysis For Real-Time Electrocardiography Telemonitoring
In order to implement a real-time electrocardiogram (ECG) telemonitoring, compressed sensing (CS) is a new technology that reduces the power consumption of biosensors and data transmission. Unfortunately, limited label data and computing resources hinder
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Xmu-Ts Systems For Nist Sre19 Cts Challenge
In this paper, we present our submitted XMU-TS system for NIST SRE19 CTS Challenge. The evaluation of this year only offers the open training condition. With the large amounts of data assimilated into training set, the diversity of training data sources i
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Dnn-Based Speech Presence Probability Estimation For Multi-Frame Single-Microphone Speech Enhancement
Multi-frame approaches for single-microphone speech enhancement, e.g., the multi-frame minimum-power-distortionless-response (MFMPDR) filter, are able to exploit speech correlations across neighboring time frames. In contrast to single-frame approaches su
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On Robust Variance Filtering And Change Of Variance Detection
This paper studies the variance filtering and change of variance (CoV) detection under multiple change points in time series signal. In real world scenarios, CoV detection can be challenging since the time series signal may contain not only outliers but a
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Quality-Of-Service Prediction For Physical-Layer Security Via Secrecy Maps
While most of the theoretical aspects of physical layer security are well understood, practical applications lag substantially behind theoretical advances. As a step towards the integration of physical-layer security aspects in the radio access system des
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A Hardware Architecture For Reconfigurable Intelligent Surfaces With Minimal Active Elements For Explicit Channel Estimation
Intelligent surfaces comprising of cost effective, nearly passive, and reconfigurable unit elements are lately gaining increasing interest due to their potential in enabling fully programmable wireless environments. They are envisioned to offer environmen
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Blind Determination Of The Number Of Sources Using Distance Correlation
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Székely et al.'s distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously prop
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A Regularization Framework For Learning Over Multitask Graphs
This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporat
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Optimal Leak Factor Selection For The Output-Constrained Leaky Filtered-Input Least Mean Square Algorithm
The leaky filtered-input least mean square (LFxLMS) algorithm is widely used in active noise control applications to minimize the degradation of attenuation performance due to output saturation distortion. However, the leak factor, which is critical in de
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Pre-Training In Deep Reinforcement Learning For Automatic Speech Recognition
Deep reinforcement learning (deep RL) is a combination of deep learning and reinforcement learning principles. it creates efficient methods that can learn by interacting with its environment. Deep RL led to breakthroughs in many complex tasks that were pr
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Effective Pipeline For Compressing Deep Object Detectors
To alleviate the deployment of deep object detectors with large model capacity and complex computation, an effective model compression pipeline is designed in this paper. Firstly, attributed to the refined soft filter pruning, 3D filters of each convoluti
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A Noninvasive Method To Detect Diabetes Mellitus And Lung Cancer Using The Stacked Sparse Autoencoder
Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently--needing professional medical instruments suc
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On The Use Of RéNyi Entropy For Optimal Window Size Computation In The Short-Time Fourier Transform
This paper investigates the determination of an optimal window length associated with the computation of the short time Fourier transform of multicomponent signals. For that purpose, the minimum of the Rényi entropy has been widely used in recent years. H
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Steganography And Its Detection In Jpeg Images Obtained With The "trunc"
Many portable imaging devices use the operation of "trunc" (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and
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Reflectance-Guided, Contrast-Accumulated Histogram Equalization
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously. To address this problem, we propose a histogram equalization-based method that adapts to the data-de
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Joint Training Of Deep Neural Networks For Multi-Channel Dereverberation And Speech Source Separation
In this paper, we propose a joint training of two deep neural networks (DNNs) for dereverberation and speech source separation. The proposed method connects the first DNN, the dereverberation part, the second DNN, and the speech source separation part in
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Hidden Markov Models For Sepsis Detection In Preterm Infants
We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model based HMM, and a recently propos
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A Hybrid Structural Sparse Error Model For Image Deblocking
Inspired by the image nonlocal self-similarity (NSS) prior, structural sparse representation (SSR) models exploit each group as the basic unit for sparse representation, which have achieved promising results in various image restoration applications. Howe
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Manet: Multi-Scale Aggregated Network For Light Field Depth Estimation
We present a novel end-to-end network, MANet, for light field depth estimation. MANet is a parameter-effective and efficient multi-scale aggregated network, which is about 3 times smaller and 3 times faster than the current top-performing method Epinet. T
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Deep Learning For Robust Power Control For Wireless Networks
Robust optimization is an important task in wireless communications, because due to fading and feedback delay there is inherent uncertainty in channel state information in a wireless environment. This paper aims to show that a deep learning approach for n
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Continual Learning Through One-Class Classification Using Vae
Artificial neural networks (ANNs) suffer from catastrophic forgetting, a sharp decrease in performance on previously learned tasks, when trained on a new task without constant rehearsal. In this paper, we propose a new method for overcoming this phenomeno
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Coincidence, Categorization, And Consolidation: Learning To Recognize Sounds With Minimal Supervision
Humans do not acquire perceptual abilities in the way we train machines. While machine learning algorithms typically operate on large collections of randomly-chosen, explicitly-labeled examples, human acquisition relies more heavily on multimodal unsuperv
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Assimilation-Based Learning Of Chaotic Dynamical Systems From Noisy And Partial Data
Despite some promising results under ideal conditions (i.e. noise-free and complete observation), learning chaotic dynamical systems from real life data is still a very challenging task. We propose a novel framework, which combines data assimilation schem
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We propose and evaluate transformer-based acoustic models for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional embedding methods and an iterated loss to enable training deep transformers. We also
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Speaker Embeddings Incorporating Acoustic Conditions For Diarization
We present our work on training speaker embeddings, especially effective for speaker diarization. For various speaker recognition tasks, extracting speaker embeddings using Deep Neural Networks (DNNs) has become major methods. These embeddings are general
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Learning Perception And Planning With Deep Active Inference
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we
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Hearing Aid Research Data Set For Acoustic Environment Recognition
State-of-the-art hearing aids (HA) are limited in recognizing acoustic environments. Much effort is spent on research to improve listening experience for HA users in every acoustic situation. There is, however, no dedicated public database to train acoust
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Recurrent Neural Audiovisual Word Embeddings For Synchronized Speech And Real-Time Mri
In this paper, the use of word embeddings for the segments found in audio and real-time magnetic resonance imaging (rtMRI) videos is addressed. In this study, word embeddings are created to store and retrieve data efficiently, and their representation pow
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1.5Gbit/S 4.9W Hyperspectral Image Encoders On A Low-Power Parallel Heterogeneous Processing Platform
This work explores the utilization of low-power heterogeneous devices for parallelizing the compute-intensive hyper-spectral and multispectral image compression CCSDS-123 entropy encoders. Multithread processing allows for the near-optimal system?s bandwi
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Improving Music Transcription By Pre-Stacking A U-Net
We propose to pre-stack a U-Net as a way of improving the polyphonic music transcription performance of various baseline Convolutional Neural Networks (CNNS). The U-Net, a network architecture based on skip-connections between layers acts as a transformat
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Privacy Aware Acoustic Scene Synthesis Using Deep Spectral Feature Inversion
Gathering information about the acoustic environment of urban areas is now possible and studied in many major cities in the world. Part of the research is to find ways to inform the citizen about its sound environment while ensuring her privacy. We study
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Cloud-Driven Multi-Way Multiple-Antenna Relay Systems: Best-User-Link Selection And Joint Mmse Detection
In this work, we present a cloud-driven uplink framework for multi-way multiple-antenna relay systems which facilitates joint linear Minimum Mean Square Error (MMSE) symbol detection in the cloud and where users are selected to simultaneously transmit to
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Sparse Branch And Bound For Exact Optimization Of L0-Norm Penalized Least Squares
We propose a global optimization approach to solve l_0-norm penalized least-squares problems, using a dedicated branch-and-bound methodology. A specific tree search strategy is built, with branching rules inspired from greedy exploration techniques. We sh
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An Lstm Based Architecture To Relate Speech Stimulus To Eeg
Modeling the relationship between natural speech and a recorded electroencephalogram (EEG) helps us understand how the brain processes speech and has various applications in neuroscience and brain-computer interfaces. In this context, so far mainly linear
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A Composite Dnn Architecture For Speech Enhancement
In speech enhancement, the use of supervised algorithms in the form of deep neural networks (DNNs) has become tremendously popular in recent years. The target function of the DNN (and the associated estimators) is often either a masking function applied t
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Low-Rank Gradient Approximation For Memory-Efficient On-Device Training Of Deep Neural Network
Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models. However, one of the major obstacles to achieving this goal is the memory limitation of mobile devices. Reducing training memory enab
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Image De-Raining Via Rdl: When Reweighted Convolutional Sparse Coding Meets Deep Learning
Over the past few decades, image de-raining has witnessed substantial progress due to the development of priors and deep learning based methods. However, few studies combine the merits of both. In this paper, we argue that domain expertise of conventional
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Minimum Latency Training Strategies For Streaming Sequence-To-Sequence Asr
Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. However, in these models, the decisions to generate tokens are delayed compared
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Lqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks
In this paper we propose the Localized Quality Aware Image Denoising (LQAID) technique for image denoising using deep convolutional neural networks (CNNs). LQAID relies on local quality estimates over global cues like noise standard deviation since the pe
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Decomposed Cyclegan For Single Image Deraining With Unpaired Data
Most previous learning-based methods required paired rain image data. In practice, however, paired rain data cannot be collected. Inspired by adopting unpaired data in task of translation, in this paper we present a new method for rain removal using unpai
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One-Bit Normalized Scatter Matrix Estimation For Complex Elliptically Symmetric Distributions
One-bit quantization has attracted attention in massive MIMO, radar, and array processing, due to its simplicity, low cost, and capability of parameter estimation. Specifically, the shape of the covariance of the unquantized data can be estimated from the