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A Hierarchical Tracker For Multi-Domain Dialogue State Tracking
The goal of Dialogue State Tracking (DST) is to estimate the current dialogue state given all the preceding conversation. Due to the increased number of state candidates, data sparsity problem is still a major hurdle for multi-domain DST. Existing methods
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Frequency Diverse Array Radar: A Closed-Form Solution To Design Weights For Desired Beampattern
In contrast to phased-array radar, frequency-diverse-array (FDA) radar transmits signals of linearly increasing frequencies across the array. As a consequence, the beampattern of an FDA radar becomes range, angle, and time dependent, which is different fr
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M-Estimators Of Scatter With Eigenvalue Shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is consid
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Speech Intelligibility Enhancement By Equalization For In-Car Applications
In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase in
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Content Vs Context: How About "walking Hand-In-Hand" For Image Clustering?
Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the power
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Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging
This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on trainin
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On End-To-End Multi-Channel Time Domain Speech Separation In Reverberant Environments
This paper introduces a new method for multi-channel time domain speech separation in reverberant environments. A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings, with no need of c
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Portfolio Cuts: A Graph-Theoretic Framework To Diversification
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis
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Hi-Mia : A Far-Field Text-Dependent Speaker Verification Database And The Baselines
This paper presents a large far-field text-dependent speaker verification database named HI-MIA. We aim to meet the data requirement for far-field microphone array based speaker verification since most of the publicly available databases are single channe
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Characterization Of A Snapshot Fourier Transform Imagingspectrometer Based On An Array Of Fabry-Perot Interferometers
This study focuses on a novel snapshot Fourier Transform imaging spectrometer based on an array of Fabry-Perot interferometers. This device fully relies on signal processing in order to provide intelligible outputs and thus requires a precise characterisa
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Maximally Energy-Concentrated Differential Window For Phase-Aware Signal Processing Using Instantaneous Frequency
The short-time Fourier transform (STFT) is widely employed in nonstationary signal analysis, whose property depends on window functions. Instantaneous frequency in STFT, the time-derivative of phase, is recently applied to many applications including spec
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Deep Multi-Region Hashing
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data w
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Beam Elimination Based On Sequentially Estimated A Posteriori Probabilities Of Winning
A robust and adaptive variable length beam selection strategy based on M-ary sequential competition was proposed in [1]. It was enhanced by the elimination of inauspicious beams during the ongoing competition to improve the efficiency and speed of the tra
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Deep neural networks (DNNs) are successful in applications with matching inference and training distributions. In real-world scenarios, DNNs have to cope with truly new data samples during inference, potentially coming from a shifted data distribution. Th
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Multiple Points Input For Convolutional Neural Networks In Replay Attack Detection
The models based on convolutional neural network (CNN) have shown remarkable performance in spoofing detection for automatic speaker verification. In order to input data into CNN-based models in mini-batch unit, the shape of all data in each mini-batch mu
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Improving Auditory Attention Decoding Performance Of Linear And Non-Linear Methods Using State-Space Model
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using aud
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Meta-Learning To Communicate: Fast End-To-End Training For Fading Channels
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach
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Lipreading Using Temporal Convolutional Networks
Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU)
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Improving Cross-Dataset Performance Of Face Presentation Attack Detection Systems Using Face Recognition Datasets
Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under
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Orthogonal Training For Text-Independent Speaker Verification
In this paper we propose orthogonal training schemes to improve the effectiveness of cosine similarity measurements in text-independent speaker verification (SV) tasks. Compared to the PLDA backend, cosine similarity is simple to compute, and it does not
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Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous approaches only exp
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On The Choice Of Graph Neural Network Architectures
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sig
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Griffin–Lim Like Phase Recovery Via Alternating Direction Method Of Multipliers
Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing. When only an amplitude spectrogram is available and no explicit information is given for the phases, the Griffin-Lim algorithm
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Bilevel Optimization Using Stationary Point Of Lower-Level Objective Function
In this letter, we address an audio signal separation problem and propose a new effective algorithm for solving a bilevel optimization in discriminative nonnegative matrix factorization (NMF). Recently, discriminative training of NMF bases has been develo
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Deepjscc: The Future Of Wireless Video Transmission
We propose a demonstration of a joint source-channel coding (JSCC) scheme, called DeepJSCC, for wireless video transmission. Unlike conventional digital communication systems, which rely on separate source and channel coding, DeepJSCC is a purely data-dri
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A Random Gossip Bmuf Process For Neural Language Modeling
Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum
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Consistency-Aware Multi-Channel Speech Enhancement Using Deep Neural Networks
This paper proposes a deep neural network (DNN)--based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal. DNN-based multi-channel speech enhancement is often conducted in the time-
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Unsupervised Training For Deep Speech Source Separation With Kullback-Leibler Divergence Based Probabilistic Loss Function
In this paper, we propose a multi-channel speech source separation method with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an alternative to a clean signal, the proposed method adopts an estimated
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A Deep Gradient Boosting Network For Optic Disc And Cup Segmentation
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-ofthe-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connect
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Feature Selection Under Orthogonal Regression With Redundancy Minimizing
Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square
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Audio Codec Enhancement With Generative Adversarial Networks
Audio codecs are typically transform-domain based and efficiently code stationary audio signals, but they struggle with speech and signals containing dense transient events such as applause. Specifically, with these two classes of signals as examples, we
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Differentiable Branching In Deep Networks For Fast Inference
In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them con
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Cross Image Cubic Interpolator For Spatially Varying Exposures
Spatially varying exposures via rolling shutter is an efficient way to capture differently exposed images for high dynamic range (HDR) scenes. Neither camera movement nor moving objects is an issue for such a captured method. However, a possible issue is
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Beyond The Dcase 2017 Challenge On Rare Sound Event Detection: A Proposal For A More Realistic Training And Test Framework
There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more
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Lie Group State Estimation Via Optimal Transport
Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints.
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Distributed Quantization For Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of
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Maximum Likelihood Estimation Of The Interference-Plus-Noise Cross Power Spectral Density Matrix For Own Voice Retrieval
In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross power spectral density (CPSD) of the noise is required. In this paper, a novel maximum
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Multitask Learning And Multistage Fusion For Dimensional Audiovisual Emotion Recognition
Due to its ability to accurately predict emotional state using multimodal features, audiovisual emotion recognition has recently gained more interest from researchers. This paper proposes two methods to predict emotional attributes from audio and visual d
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Joint Resource Allocation And Routing For Service Function Chaining With In-Subnetwork Processing
Network Function Virtualization (NFV) is an efficient approach to simplify and accelerate the deployment of diverse network services. A critical challenge lies in mapping Virtual Network Functions (VNFs) to high-volume servers, resource allocation, and tr
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On-The-Fly Feature Selection And Classification With Application To Civic Engagement Platforms
Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framewo
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Attention Mechanism Enhanced Kernel Prediction Networks For Denoising Of Burst Images
Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adop
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This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)--ba
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A Dsp Acceleration Framework For Software-Defined Radios On X86_64
This paper presents a DSP acceleration and assessment framework targeting SDR platforms on x86_64 architectures. Driven by the potential of rapid prototyping and evaluation of breakthrough concepts that these platforms provide, our work builds upon the we
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Constant Envelope Massive Mimo-Ofdm Precoding: An Improved Formulation And Solution
Constant Envelope (CE) precoding is an efficient technique for systems based on massive antenna arrays since the constant amplitude of the transmit signal facilitates the use of power efficient non-linear transmitter circuitry, such as power amplifiers (P
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A Cross-Task Transfer Learning Approach To Adapting Deep Speech Enhancement Models To Unseen Background Noise Using Paired Senone Classifiers
We propose an environment adaptation approach that improves deep speech enhancement models via minimizing the Kullback- Leibler divergence between posterior probabilities produced by a multi-condition senone classifier (teacher) fed with noisy speech feat
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Optimum Kernel Particle Filter For Asymmetric Laplace Noise
In this paper we present on-line Bayesian filtering methods for time series models corrupted by asymmetric Laplace noise. An optimum kernel particle filter is designed for the general asymmetric case, and its performance is compared to that of a tradition