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Enhance Part-Based Model For Person Re-Identification With Fused Multi-Scale Features
In recent years, part-based models have been verified their effectiveness for person Re-identification (Re-ID). Since they learn an embedding only by partitioning single-scale features of the highest layer in the network, their performances highly depend
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Channel Covariance Estimation In Multiuser Massive Mimo Systems With An Approach Based On Infinite Dimensional Hilbert Spaces
We propose a novel algorithm to estimate the channel covariance matrix of a desired user in multiuser massive MIMO systems. The algorithm uses only knowledge of the array response and rough knowledge of the angular support of the incoming signals, which a
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A Theoretical Basis For Practitioners Heuristic 1/N And Long-Only Quintile Portfolio
The heuristic 1/N (equally weighted) portfolio and long-only quintile portfolio are both popular simple strategies in financial investment. In the 1/N portfolio, a fraction of 1/N of wealth is allocated to each of the N available assets. In the long-only
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A Discriminative Condition-Aware Backend For Speaker Verification
We present a scoring approach for speaker verification that mimics the standard PLDA-based backend process used in most current speaker verification systems. However, unlike the standard backends, all parameters of the model are jointly trained to optimiz
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Kernel Ridge Regression With Autocorrelation Prior: Optimal Model And Cross-Validation
Kernel regression problem with autocorrelation prior is discussed in this paper. We revealed the optimal model of the kernel ridge regression in terms of the expected generalization error under the assumed autocorrelation prior. This result agrees with th
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Slicenet: Slice-Wise 3D Shapes Reconstruction From Single Image
3D object reconstruction from a single image is a highly ill-posed problem, requiring strong prior knowledge of 3D shapes. Deep learning methods are popular for this task. Especially, most works utilized 3D deconvolution to generate 3D shapes. However, th
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Sed-Mdd: Towards Sentence Dependent End-To-End Mispronunciation Detection And Diagnosis
A mispronunciation detection and diagnosis (MD&D) system typically consists of multiple stages, such as an acoustic model, a language model and a Viterbi decoder. In order to integrate these stages, we propose SED-MDD, an end-to-end model for sentence dep
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Cross-Lingual Topic Prediction For Speech Using Translations
Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount of speech in the low-resource language is paired with text translations in
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Discriminant And Sparsity Based Least Squares Regression With L1 Regularization For Feature Representation
Least squares regression (LSR) has two main issues that greatly limits the improvement of performance: 1) The target matrix is too rigid to learn a discriminative projection matrix leading to a large regression error; 2) the underlying geometric structure
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Time-Frequency Analysis Of Unimodal Sensory Processing In Autism Spectrum Disorder
This work summarizes the results of a time-frequency analysis of sensory processing in young adults with Autism Spectrum Disorder via continuous wavelet transform. The sensory tasks consisted of two blocks of unimodal sensory stimuli of the same type (i.e
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Roimix: Proposal-Fusion Among Multiple Images For Underwater Object Detection
Generic object detection algorithms have proven their excellent performance in recent years. However, object detection on underwater datasets is still less explored. In contrast to generic datasets, underwater images usually have color shift and low contr
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Duration Robust Weakly Supervised Sound Event Detection
Task 4 of the DCASE2018 challenge demonstrated that substantially more research is needed for a real-world application of sound event detection. Analyzing the challenge results it can be seen that most successful models are biased towards predicting long
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Federated Learning With Mutually Cooperating Devices: A Consensus Approach Towards Server-Less Model Optimization
Abstract Federated learning (FL) is emerging as a new paradigm for training a machine learning model in cooperative networks. The model parameters are optimized collectively by large populations of interconnected devices, acting as cooperative learners th
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Visually Guided Self Supervised Learning Of Speech Representations
Self supervised representation learning has recently attracted a lot of research interest for both the audio and visual modalities. However, most works typically focus on a particular modality or feature alone and there has been very limited work that stu
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Improving Proper Noun Recognition In End-To-End Asr By Customization Of The Mwer Loss Criterion
Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pronunciation similar to that of a more common word. Unlike conventional ASR
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Mining Effective Negative Training Samples For Keyword Spotting
Max-pooling neural network architectures have been proven to be useful for keyword spotting (KWS), but standard training methods suffer from a class-imbalance problem when using all frames from negative utterances. To address the problem, we propose an in
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Target Parameter Estimation Via One-Bit Pmcw Radar
We consider the problem of phase modulated continuous wave (PMCW) radar signal processing when the receiver utilizes one-bit sampling with known time-varying thresholds. We formulate the target parameter estimation problem as a sparse signal recovery prob
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Attention-Based Asr With Lightweight And Dynamic Convolutions
End-to-end (E2E) automatic speech recognition (ASR) with sequence- to-sequence models has gained attention because of its simple model training compared with conventional hidden Markov model based ASR. Recently, several studies report the state-of-the-art
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Transmit Beamforming Design With Received-Interference Power Constraints: The Zero-Forcing Relaxation
The use of multi-antenna transmitters is emerging as an essential technology of the future wireless communication systems. While Zero-Forcing Beamforming (ZFB) has become the most popular low-complexity transmit beamforming design, it has some drawbacks b
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Bp-Vb-Ep Based Static And Dynamic Sparse Bayesian Learning With Kronecker Structured Dictionaries
In many applications such as massive multi-input multi-output (MIMO) radar, massive MIMO channel estimation, speech processing, image and video processing, the received signals are tensors. In such applications, utilizing techniques from tensor algebra ca
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Spatio-Temporal And Geometry Constrained Network For Automobile Visual Odometry
Visual odometry (VO) is an essence of vision-based localization and mapping system where existing learning-based approaches utilize CNN and RNN to model camera motion and gain promising results. However, these methods lack full use of the relationship bet
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Demystifying Tasnet: A Dissecting Approach
In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradua
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Bayesian Estimation Of Plda With Noisy Training Labels, With Applications To Speaker Verification
This paper proposes a method for Bayesian estimation of probabilistic linear discriminant analysis (PLDA) when training labels are noisy. Label errors can be expected during e.g. large or distributed data collections, or for crowd-sourced data labeling. B
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Speaker Diarization With Region Proposal Network
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. The standard diarization systems can achieve satisfactory results in various scenarios, but they are composed of sever
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Weighted Null Vector Initialization And Its Application To Phase Retrieval
Phase retrieval problem is an nonlinear inverse problem of recovering real- or complex-valued signal from quadratic measurements, which arises in various applications. The best-known algorithms for solving this problem are non-convex methods starting with
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Power Spectrum Optimization For Capacity Of The Extended Spectrum Hybrid Fiber Coax Network
Capacity requirements of the fixed access network keep increasing towards multi-gigabit connections. For Hybrid Fiber Coaxial (HFC) networks, aggregated rates around 30 Gbit/s can be achieved by increasing the DOCSIS spectrum to 3GHz, assuming a spectral
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Generative Pre-Training For Speech With Autoregressive Predictive Coding
Learning meaningful and general representations from unannotated speech that are applicable to a wide range of tasks remains challenging. In this paper we propose to use autoregressive predictive coding (APC), a recently proposed self-supervised objective
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An Attention-Based Joint Acoustic And Text On-Device End-To-End Model
Recently, we introduced a two-pass on-device end-to-end (E2E) speech recognition model, which runs RNN-T in the first-pass and then rescores/redecodes the result using a noncausal Listen, Attend and Spell (LAS) decoder. This on-device model obtained simil
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Non-Gaussian Ble-Based Indoor Localization Via Gaussian Sum Filtering Coupled With Wasserstein Distance
With recent breakthroughs in signal processing, communication and networking systems, we are more and more surrounded by smart connected devices empowered by the Internet of Thing (IoT). Bluetooth Low Energy (BLE) is considered as the main-stream technolo
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An Efficient Augmented Lagrangian-Based Method For Linear Equality-Constrained Lasso
Variable selection is one of the most important tasks in statistics and machine learning. To incorporate more prior information about the regression coefficients, various constrained Lasso models have been proposed in the literature. Compared with the cla
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Pixel-Wise Linear/Nonlinear Nonnegative Matrix Factorization For Unmixing Of Hyperspectral Data
Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective nonnegative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing; However, it suffers several issues tha
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Atomic Norm Denoising In Blind Two-Dimensional Super-Resolution
In this work, we develop a new framework for denoising in blind two-dimensional (2D) super-resolution that recovers a set of 2D continuous parameters as well as unknown waveforms from noisy samples. We apply the atomic norm to denoise a weighted sum of ti
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Phoneme Boundary Detection Using Learnable Segmental Features
Phoneme boundary detection plays an essential first step for a variety of speech processing applications such as speaker diarization, speech science, keyword spotting, etc. In this work, we propose a neural architecture coupled with a parameterized struct
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The Role Of Annotation Fusion Methods In The Study Of Human-Reported Emotion Experience During Music Listening
Music is a universally-enjoyed art form, but listeners often respond to it in tremendously different ways. The same song can bring one person great joy and another deep sorrow. This paper focuses on modeling human music experience at the group level. In t
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Talker-Independent Speaker Separation In Reverberant Conditions
Speaker separation refers to the task of separating a mixture signal comprising two or more speakers. Impressive advances have been made recently in deep learning based talker-independent speaker separation. But such advances are achieved in anechoic cond
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Robust And Computationally-Efficient Anomaly Detection Using Powers-Of-Two Networks
Robust and computationally efficient anomaly detection in videos is a problem in video surveillance systems. We propose a technique to increase robustness and reduce computational complexity in a Convolutional Neural Network (CNN) based anomaly detector t
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A Dataset For Measuring Reading Levels In India At Scale
One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this ta
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Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Spec
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Anomaly Detection With Training Data In Hyperspectral Imagery
In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of fals
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Classifying Anomalies For Network Security
Detecting and classifying anomalous behaviors in computer networks remains a formidable challenge. This work outlines a machine learning technique that uses deep neural networks to detect and classify a variety of network attacks. Our approach is based on
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Resilient To Byzantine Attacks Finite-Sum Optimization Over Networks
This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different rob
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A Comparative Study Of Western And Chinese Classical Music Based On Soundscape Models
Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this questi
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Pitch Estimation Via Self-Supervision
We present a method to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. In contrast to existing methods, our neural network can be fully trained only on unlabeled data, using self-supervision. A tiny amount of
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Load Management With Predictions Of Solar Energy Production For Cloud Data Centers
Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raise
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Enhanced Method Of Audio Coding Using Cnn-Based Spectral Recovery With Adaptive Structure
A process of spectral recovery can enhance the performance of transform-based audio coding by transmitting only a portion of spectral data and recovering the missing spectral data in the decoder. This study proposes an enhanced method of audio coding base
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Urtis: A Small 3D Imaging Sonar Sensor For Robotic Applications
State-of-the-art autonomous vehicles mainly rely on optical sensors to perceive their environment. However, the performance of these sensors worsens dramatically in environments where airborne particles are present. Sonar sensors rely on acoustic waves wh
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Near Capacity Rcqd Constellations For Papr Reduction Of Ofdm Systems
We investigate an optimized blind SeLected Mapping (SLM) algorithm to reduce the Peak-to-Average Power Ratio (PAPR) for Orthogonal Frequency Division Multiplexing (OFDM) systems with Signal Space Diversity (SSD). Several phase sequences based on two Rotat
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Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has mad
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End-To-End Speech Translation With Self-Contained Vocabulary Manipulation
In machine translation, vocabulary manipulation is a way to reduce the target vocabulary based on the source sentence and the word dictionary, which is effective to lower latency during inference for text translation in industrial application. But vocabul