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Continual Learning For Infinite Hierarchical Change-Point Detection
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is to infer the posterior distribution of the location of change points. Howe
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Sound Event Detection In Synthetic Domestic Environments
We present a comparative analysis of the performance of state-of-the-art sound event detection systems. In particular, we study the robustness of the systems to noise and signal degradation, which is known to impact model generalization. Our analysis is b
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Instance-Based Model Adaptation For Direct Speech Translation
Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora. We tackle this limitation with a method to improve data exploit
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Optimal Window Design For Joint Spatial-Spectral Domain Filtering Of Signals On The Sphere
We present the optimal design of an azimuthally symmetric window signal for carrying out joint spatial-spectral domain filtering of a spherical (source) signal contaminated by a realization of an anisotropic noise process. The resulting window is used in
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Keyword Search For Sign Language
Keyword search is the search for a written query in an archive, which is often assumed to be a collection from a spoken language. Yet, the main languages of the Deaf, i.e. sign languages, are mostly neglected in this definition due to being visual languag
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Multichannel Signal Processing For Road Surface Identification
The development of autonomous or semi-autonomous car technology is attracting much attention in recent years. An important aspect of this research is automatic identification of road surfaces, since adjustments can be made to improve the safety of the car
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Improving Lpcnet-Based Text-To-Speech With Linear Prediction-Structured Mixture Density Network
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining
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Information Theoretic Approach For Waveform Design In Coexisting Mimo Radar And Mimo Communications
We investigate waveform design for coexistence between a multiple input multiple-output (MIMO) radar and MIMO communications (MRMC), with a radar-centric criterion that leads to a minimal interference in the communications system. The communications use t
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Channel Invariant Speaker Embedding Learning With Joint Multi-Task And Adversarial Training
Using deep neural network to extract speaker embedding has significantly improved the speaker verification task. However, such embeddings are still vulnerable to channel variability. Previous works have used adversarial training to suppress channel inform
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Effect Of Undersampling On Non-Negative Blind Deconvolution With Autoregressive Filters
This paper considers the problem of blind deconvolution where the input signal is non-negative and sparse, and the unknown convolutional kernel is a first order autoregressive filter. Our objective is to understand if it is possible to recover both the si
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Image Fusion Using Joint Sparse Representations And Coupled Dictionary Learning
The image fusion problem consists in combining complementary parts of multiple images captured, for example, with different focal settings into one image of higher quality. This requires the identification of the sharpest areas in sets of input images. Re
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Effects Of Spectral Tilt On Listeners' Preferences And Intelligibility
High intelligibility can be achieved when listening to synthetic or artificially-produced speech under adverse conditions. But can listener preferences reveal any extra information when intelligibility is at ceiling? This paper describes a real-time speec
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The Picasso Algorithm For Bayesian Localization Via Paired Comparisons In A Union Of Subspaces Model
We develop a framework for localizing an unknown point $\w$ using paired comparisons of the form ``$\w$ is closer to point $\x_i$ than to $\x_j$'' when the points lie in a union of known subspaces. This model, which extends a broad class of existing metho
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Polyphonic Sound Event Detection Using Transposed Convolutional Recurrent Neural Network
In this paper we propose a Transposed Convolutional Recurrent Neural Network (TCRNN) architecture for polyphonic sound event recognition. Transposed convolution layer, which caries out a regular convolution operation but reverts the spatial transformation
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Precise Performance Analysis Of The Box-Elastic Net Under Matrix Uncertainties
In this letter, we consider the problem of recovering an unknown sparse signal from noisy linear measurements, using an enhanced version of the popular Elastic-Net (EN) method.We modify the EN by adding a box-constraint, and we call it the Box-Elastic Net
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Real-Time Epileptic Seizure Detection During Sleep Using Passive Infrared (Pir) Sensors
According to World Health Organization (WHO), millions of people suffer from epilepsy, which is a chronic disorder of the brain. Sudden Unexplained Death in Epilepsy (SUDEP) is considered as one of the most dangerous threats to the patients who suffer fro
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Self-Tuning Algorithms For Multisensor-Multitarget Tracking Using Belief Propagation
Situation-aware technologies enabled by multitarget tracking algorithms will create new services and applications in emerging fields such as autonomous navigation and maritime surveillance. The system models underlying multitarget tracking algorithms ofte
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A Single-Wavelength Real-Time Material-Sensing Camera Based On Time-Of-Flight Measurements
Time-of-Flight (ToF) cameras provide a fast and robust way of acquiring the 3D shape of real scenes. Dense depth images can be generated at tens of frame per second. 3D shapes can be then segmented and objects classified, but can we directly sense the obj
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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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Decidable Variable-Rate Dataflow For Heterogeneous Signal Processing Systems
Dynamic dataflow models of computation have become widely used through their adoption to popular programming frameworks such as TensorFlow and GNU Radio. Although dynamic dataflow models offer more programming freedom, they lack analyzability compared to
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A Neural Network For Monaural Intrusive Speech Intelligibility Prediction
Monaural intrusive speech intelligibility prediction (SIP) methods aim to predict the speech intelligibility (SI) of a single-microphone noisy and/or processed speech signal using the underlying clean speech signal. In the present work, we propose a neura
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Tracing Network Evolution Using The Parafac2 Model
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is
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Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense Mmwave Networks
The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communicati
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Detection Of Malicious Vbscript Using Static And Dynamic Analysis With Recurrent Deep Learning
Attackers have used malicious VBScripts as an important computer infection vector. In this study, we explore a system that employs both static and dynamic analysis to detect malicious VBScripts. For the static analysis, we investigate two deep recurrent m
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Media Classification With Bayesian Optimization And Vapnik-Chervonenkis (Vc) Bounds
The automatic classification of content is an essential requirement for multimedia applications. Present research for audio-based classifiers uses short- and long-term analysis of signals, with temporal and spectral features. In our prior study, we presen
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Prediction Of Individual Progression Rate In Parkinson’S Disease Using Clinical Measures And Biomechanical Measures Of Gait And Postural Stability
Parkinson?s disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study
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Active Control Of Line Spectral Noise With Simultaneous Secondary Path Modeling Without Auxiliary Noise
Online secondary path modeling is appealing for most active noise control systems due to its benefit of effective tracking of the varying acoustic environment and possible variation of the control sources and sensors. However, the usually utilized additiv
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Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring
This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data j
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Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably And Efficiently
Multi-channel sparse blind deconvolution refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. It is challenging to learn the filter efficiently due to the bilinear structu
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Oov Recovery With Efficient 2Nd Pass Decoding And Open-Vocabulary Word-Level Rnnlm Rescoring For Hybrid Asr
In this paper, we investigate out-of-vocabulary (OOV) word recovery in word-based hybrid automatic speech recognition (ASR) systems, with emphasis on dynamic vocabulary expansion for both Weight Finite State Transducer (WFST)-based decoding and word-level
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Fast Clustering With Co-Clustering Via Discrete Non-Negative Matrix Factorization For Image Identification
How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is