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Training Code-Switching Language Model With Monolingual Data
A lack of code-switching data complicates the training of code-switching (CS) language models. We propose an approach to train such CS language models on monolingual data only. By constraining and normalizing the output projection matrix in RNN-based lang
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Graph Metric Learning Via Gershgorin Disc Alignment
We propose a fast general projection-free metric learning framework, where the minimization objective $min_{M in cS} Q(M)$ is a convex differentiable function of the metric matrix $M$, and $M$ resides in the set $cS$ of generalized graph Laplacian
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Pose Refinement: Bridging The Gap Between Unsupervised Learning And Geometric Methods For Visual Odometry
Unsupervised Learning based monocular visual odometry (VO) has lately drawn significant attention owing to its potential in label-free leaning ability and robustness to camera parameters and environmental variations. However, due to the lack of pose optim
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Spatial Attentional Bilinear 3D Convolutional Network For Video-Based Autism Spectrum Disorder Detection
Video-based Autism Spectrum Disorder (ASD) detection is a challenge to most video classification networks due to the high degree of similarity between categories. Bilinear pooling is a second-order method, which is widely used in fine-grained visual recog
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Efficient Constrained Encoders Correcting A Single Nucleotide Edit In Dna Storage
A nucleotide substitution is said to occur when a base in {A, T} is substituted for a base in {C, G}, or vice versa. Recent experiment (Heckel et al. 2019) showed that a nucleotide substitution occurs with a significantly higher probability that other sub
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Transformer-Based Text-To-Speech With Weighted Forced Attention
This paper investigates state-of-the-art Transformer- and FastSpeech-based high-fidelity neural text-to-speech (TTS) with full-context label input for pitch accent languages. The aim is to realize faster training than conventional Tacotron-based models. I
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Redundant Convolutional Network With Attention Mechanism For Monaural Speech Enhancement
The redundant convolutional encoder decoder network has proven useful in speech enhancement tasks. It can capture localized time-frequency details of speech signals through both the fully convolutional network structure and feature selection capability re
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Predicting Word Error Rate For Reverberant Speech
Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parameter that can be estimated from the acoustic impulse response, is correlated
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Multi-Label Consistent Convolutional Transform Learning: Application To Non-Intrusive Load Monitoring
Convolutional transform learning is an unsupervised framework we introduced recently, for feature generation based on learnt convolutions. In this work, we propose a supervised formulation for convolutional transform so as to address the multi-label class
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Unsupervised Multiple Source Localization Using Relative Harmonic Coefficients
This paper presents an unsupervised multi-source localization algorithm using a recently introduced feature called the relative harmonic coefficients. We derive a closed-form expression of the feature and briefly summarize its unique properties. We then e
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Data Augmentation Using Empirical Mode Decomposition On Neural Networks To Classify Impact Noise In Vehicle
In a vehicle, impact noise may occur during steering action due to clearance between parts of steering systems. Via structural path the noise is perceived by the drivers? ears and it can be the cause of a repair campaign. It is importatnt to know where th
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Learning Blind Denoising Network For Noisy Image Deblurring
Noisy image deblurring is to recover the blurry image in the presence of the random noise. The key to this problem is to know the noise level in each iteration. The existing methods manually adjust the regularization parameter for varying noise levels, wh
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Global Traffic State Recovery Via Local Observations With Generative Adversarial Networks
Traffic signal control for a large-scale traffic network is one challenging problem in intelligent transportation systems (ITS). High communication overheads are typically required to achieve the optimal control of the traffic signals in multiple road int
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Low-Rank Approximation Of Matrices Via A Rank-Revealing Factorization With Randomization
Given a matrix A with numerical rank k, the two-sided orthogonal decomposition (TSOD) computes a factorization A = UDV^T , where U and V are unitary, and D is (upper/lower) triangular. TSOD is rank-revealing as the middle factor D reveals the rank of A. T
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In-Network Caching For Hybrid Satellite-Terrestrial Networks Using Deep Reinforcement Learning
Large number of redundant requests in wireless networks have lead to the hybrid satellite-terrestrial networks, where a satellite is used for content placement at edge caches at base stations (BSs), thereby reducing backhaul link usage. In this paper, we
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Multi-Task Center-Of-Pressure Metrics Estimation From Skeleton Using Graph Convolutional Network
Center of pressure (COP) is an important measurement of postural and gait control in human biomechanical studies. A vision-based estimation of COP metrics offers a way to obtain these gold-standard metrics for the detection of balance and gait problems. I
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An Ensemble Based Approach For Generalized Detection Of Spoofing Attacks To Automatic Speaker Recognizers
As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection f
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Selective Convolutional Network: An Efficient Object Detector With Ignoring Background
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce the prohibitive computational complexity by a novel attempt at attention. T
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Gaussian Process Imputation Of Multiple Financial Series
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed. We fo
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Low-Rank Mmwave Mimo Channel Estimation In One-Bit Receivers
Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers and the use of l
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Just Noticeable Distortion Based Perceptually Lossless Intra Coding
Perceptual video coding plays a very important role in video codec optimization aiming at removing the perceptual redundancies in video content. In this paper, a just noticeable distortion (JND) guided perceptually lossless coding framework is proposed fo
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Forecasting Sparse Traffic Congestion Patterns Using Message-Passing Rnns
The ability to forecast traffic congestion ahead of time given road conditions has remained a prominent problem in road traffic analysis. In this work, we leverage mobility traces of public transport vehicles tracked by the New York City MTA and formulate
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Attentive Modality Hopping Mechanism For Speech Emotion Recognition
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by independently fusing the knowledge from the various modalities
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Low-Complexity Lstm-Assisted Bit-Flipping Algorithm For Successive Cancellation List Polar Decoder
Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Although the cyclic redundancy check (CRC)- assisted successive cancellation lis
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Subjective Quality Estimation Using Pesq For Hands-Free Terminals
Previous reports have mentioned the possibility that subjective quality of the echo-suppressed speech signal can be estimated based on perceptual evaluation of speech quality (PESQ), but there are few experimental results. We propose third-party listening
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Prediction Of Voicing And The F0 Contour From Electromagnetic Articulography Data For Articulation-To-Speech Synthesis
Articulation-to-speech synthesis based solely on supraglottal articulation requires some sort of intonation control. This paper examines to what extent the f0 contour of an utterance can be predicted from such supraglottal articulation data. To that end,
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Bayesian Multiple Change-Point Detection With Limited Communication
Several modern applications involve large-scale sensor networks for statistical inference. For example, such sensor networks are of significant interest for Internet of Things applications. In this paper, we consider Bayesian multiple change-point detecti
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Hand-3D-Studio: A New Multi-View System For 3D Hand Reconstruction
This paper proposes a new system named as Hand-3D-Studio to capture the 3D hand pose and shape information. Our system includes 15 synchronized DSLR cameras, which can acquire high quality multi-view 4K resolution color images in a circular manner. We the
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Source Enumeration Via Toeplitz Matrix Completion
This paper addresses the problem of source enumeration by an array of sensors in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances, referred to non-iid noise hereafter, when the sources are unc
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Multimodal Transformer Fusion For Continuous Emotion Recognition
Multimodal fusion increases the performance of emotion recognition because of the complementarity of different modalities. Compared with decision level and feature level fusion, model level fusion makes better use of the advantages of deep neural networks
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End-To-End Training Of Time Domain Audio Separation And Recognition
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multispeaker speech recognition. However, up until now, state-of-the-art neural network?based time domain source separation has not
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Interrupted And Cascaded Permutation Invariant Training For Speech Separation
Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the minimum cost label assignments dynamically, very few studies considered the sep
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Dnn-Based Distributed Multichannel Mask Estimation For Speech Enhancement In Microphone Arrays
Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions in the real world. Distributed sensor arrays that consider several devices with a few microphones is a viable solution which
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Performance Analysis For Path Attenuation Estimation Of Microwave Signals Due To Rainfall And Beyond
The attenuation of microwave signals can be used for meteorological observations. For example, the received signal level (RSL) of backhaul links of cellular systems, which usually has quantization error of 0.1 dB or more for commercial systems, has been u
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A Bidirectional Context Propagation Network For Urine Sediment Particle Detection In Microscopic Images
The microscopic urine sediment examination is a crucial part in the evaluation of renal and urinary tract diseases. Recently, there are emerging CNNs-based detectors to detect the urine sediment particles in an end-to-end manner. However, it is not very c
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Hierarchical Attention Transfer Networks For Depression Assessment From Speech
A growing area of mental health research is the search for speech-based objective markers for conditions such as depression. However, when combined with machine learning, this search can be challenging due to a limited amount of annotated training data. I
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Group-Utility Metric For Efficient Sensor Selection And Removal In Lcmv Beamformers
In sensor arrays or sensor networks, tracking each sensor?s utility helps in excluding those which do not sufficiently contribute to the task at hand, thereby reducing energy consumption or avoiding model overfitting. In a linearly-constrained minimum var
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Optimal Sampling Rate And Bandwidth Of Bandlimited Signals - An Algorithmic Perspective
The bandwidth of a bandlimited signal is a key quantity that is relevant in numerous applications. For example, it determines the minimum sampling rate that is necessary to reconstruct a bandlimited signal from its samples. In this paper we study if it is
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Learning Spatio-Temporal Representations With Temporal Squeeze Pooling
In this paper, we propose a new video representation learning method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set of few images, named Squeezed Images
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Improving Sample-Efficiency In Reinforcement Learning For Dialogue Systems By Using Trainable-Action-Mask
By interacting with human and learning from reward signals, reinforcement learning is an ideal way to build conversational AI. Concerning the expenses of real-users' responses, improving sample-efficiency has been the key issue when applying reinforcement
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Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis
Depression is a mental illness that may be harmful to an individual?s health. Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection. Typically, depre
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Cross Lingual Transfer Learning For Zero-Resource Domain Adaptation
We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual mode
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Joint Scheduling And Beamforming For Delay Sensitive Traffic With Priorities And Deadlines
Packet scheduling in 5G networks can significantly affect the perfor- mance of beamforming techniques since the allocation of multiple users to the same time-frequency block causes interference between users. A combination of beamforming and scheduling ca
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Self-Driven Graph Volterra Models For Higher-Order Link Prediction
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-orde
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Blind Bounded Source Separation Using Neural Networks With Local Learning Rules
An important problem encountered by both natural and engineered signal processing systems is blind source separation. In many instances of the problem, the sources are bounded by their nature and known to be so, even though the particular bound may not be
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Robust Covariance Matrix Estimation And Portfolio Allocation: The Case Of Non-Homogeneous Assets
This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same improvem
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A Deep Learning Approach To Object Affordance Segmentation
Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the
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Alignment-Length Synchronous Decoding For Rnn Transducer
We present a beam decoding strategy for recurrent neural network transducers which has the characteristic that all competing hypotheses within the beam have the same alignment length (number of output symbols plus BLANK symbols). We contrast the proposed
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Generalized Spatial Modulation For Wireless Terabits Systems Under Sub-Thz Channel With Rf Impairments
Multiple-Input Multiple-Output (MIMO) technique with Index Modulation (IM) over sub-TeraHertz (sub-THz) bands represent a promising solution to design new wireless ultra-high data rate systems. However, the system design over sub-THz bands suffers from ma