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Robust Hybrid Precoding For Interference Exploitation In Massive Mimo Systems
In this paper, we consider a multiuser massive MIMO system with hybrid analog-digital precoding architecture. The phase shifters in the hybrid precoding architecture are assumed to be imperfect, where the true values of both phase and magnitude of the pha
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Estimating Centrality Blindly From Low-Pass Filtered Graph Signals
This paper considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown low-pass graph filter excited with influences governed by a sparse sub-graph. This model is compatible with a number of da
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Opportunistic Use Of Gnss Signals To Characterize The Environment By Means Of Machine Learning Based Processing
GNSS is widely used to provide positions in an absolute reference frame in Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), where GNSS is merged with the information provided by other sensors. Even if the main goal of GNSS signal process
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Human-Machine Collaboration For Medical Image Segmentation
Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-based approaches achieve state-of-the-art in the majority of image segmentation benchmarks. However, end-to-end training of such models requires sufficient annotation
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Data-Driven Wind Speed Estimation Using Multiple Microphones
A deep neural network (DNN) based approach for estimating the speed of airflows using closely-spaced microphones is proposed. The spatial characteristics of wind noise measured with a small-aperture array are exploited, i.e., the low-frequency spatial coh
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Unsupervised Pretraining Transfers Well Across Languages
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive pr
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Object Surface Estimation From Radar Images
In this paper we develop a deep neural network (DNN) method for estimating the object surface from radar 2D image (azimuth-range). The DNN is designed to maintain the input image angular resolution and produces two outputs per each angle, which are a clas
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Inverse Multiple Scattering With Phaseless Measurements
We study the problem of reconstructing an object from phaseless measurements in the context of inverse multiple scattering. Our formulation explicitly decouples the variables that represent the unknown object image and the unknown phase, respectively, in
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Accuracy-Robustness Trade-Off For Positively Weighted Neural Networks
This work proposes a new learning strategy for training a feedforward neural network subject to spectral norm and nonnegativity constraints. Our primary goal is to control the Lipschitz constant of the network in order to make it robust against adversaria
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Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
Despite the ability to produce human-level speech for in-domain text, attention-based end-to-end text-to-speech (TTS) systems suffer from text alignment failures that increase in frequency for out-of-domain text. We show that these failures can be address
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Building Firmly Nonexpansive Convolutional Neural Networks
Building nonexpansive Convolutional Neural Networks (CNNs) is a challenging problem that has recently gained a lot of attention from the image processing community. In particular, it appears to be the key to obtain convergent Plug-and-Play algorithms. Thi
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Autoregressive Parameter Estimation With Dnn-Based Pre-Processing
In this paper, a method for estimating the autoregressive parameters from a signal segment is proposed. The method is based on a deep neural network (DNN) in combination with the classical Levinson-Durbin recursion (LDR). The DNN acts as a pre-processor f
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Variable Projection For Multiple Frequency Estimation
The estimation of the frequencies of multiple complex sinusoids in the presence of noise is required in many applications such as sonar, speech processing, communications, and power systems. According to previous works [1,2], this problem can be reformula
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Mixup Multi-Attention Multi-Tasking Model For Early-Stage Leukemia Identification
Recently, several image processing and deep learning techniques have been applied to automate the detection of Acute Lymphoblastic Leukemia cells (ALL). However, most of them have consistently focused on classification mature stage cell images into binary
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Effectiveness Of Self-Supervised Pre-Training For Asr
We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wa
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Lstm-Based One-Pass Decoder For Low-Latency Streaming
Current state-of-the-art models based on Long-Short Term Memory (LSTM) networks have been extensively used in automatic speech recognition (ASR) to improve the performance of these systems. However, using them under a streaming setup is not straightforwar
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Towards An Intelligent Microscope: Adaptively Learned Illumination For Optimal Sample Classification
Recent machine learning techniques have dramatically changed how we process digital images. However, the way in which we capture images is still largely driven by human intuition and experience. This restriction is in part due to the many available degree
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Cochlear Signal Processing: A Platform For Learning The Fundamentals Of Digital Signal Processing
The first digital signal processing course in most electrical engineering programmes around the world tends to be a significant jump in abstraction for most students. This is a consequence of them being introduced to a large number of mathematical concept
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The Graphon Fourier Transform
In many network problems, graphs may change by the addition of nodes, or the same problem may need to be solved in multiple similar graphs. This generates inefficiency, as analyses and systems that are not transferable have to be redesigned. To address th
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Gradient Delay Analysis In Asynchronous Distributed Optimization
Gradient-based algorithms play an important role in solving a wide range of stochastic optimization problems. In recent years, implementing such schemes in parallel has become the new paradigm. In this work, we focus on the asynchronous implementation of
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Data-Driven Harmonic Filters For Audio Representation Learning
We introduce a trainable front-end module for audio representation learning that exploits the inherent harmonic structure of audio signals. The proposed architecture, composed of a set of filters, compels the subsequent network to capture harmonic relatio
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A Priori Estimates Of The Generalization Error For Autoencoders
Autoencoder is a machine learning model which aims for dimensionality reduction, by reconstructing its input through a bottleneck with lower dimension than the input. It is among the most popular models used in unsupervised learning and semi-supervised le
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One-Bit Sampling In Fractional Fourier Domain
The fractional Fourier transform has found applications in a variety of topics linked with science and engineering. In this context, sampling theory is one of the most well-studied subjects. Since the fractional Fourier transform or the FrFT generalizes t
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Deep Joint Source-Channel Coding Of Images With Feedback
We consider wireless transmission of images in the presence of channel output feedback, by introducing an autoencoder-based deep joint source-channel coding (JSCC) scheme. We achieve impressive results in terms of the end-to-end reconstruction quality for
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Image Processing In Dna
The main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual
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An Adaptive Linear Estimator Based Approach To Bi-Directional Motion Compensated Prediction
Bi-directional motion compensated prediction is widely utilized in video coding. Conventionally, the encoder searches for two motion vectors pointing to reference frames in both directions, and transmits these motion vectors to the decoder. Recognizing th
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Channel Attention Based Generative Network For Robust Visual Tracking
In recent years, Siamese trackers have achieved great success in visual tracking. Siamese networks can achieve competitive performance in both accuracy and speed. However, they may suffer from the performance degradation due to the case of large pose vari
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Pevd-Based Speech Enhancement In Reverberant Environments
The enhancement of noisy speech is important for applications involving human-to-human interactions, such as telecommunications and hearing aids, as well as human-to-machine interactions, such as voice-controlled systems and robot audition. In this work,
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Coupled Training Of Sequence-To-Sequence Models For Accented Speech Recognition
Accented speech poses significant challenges for state-of-the-art automatic speech recognition (ASR) systems. Accent is a property of speech that lasts throughout an utterance in varying degrees of strength. This makes it hard to isolate the influence of
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Learning From Dances: Pose-Invariant Re-Identification For Multi-Person Tracking
Most existing multi-person tracking approaches rely on appearance based re-identification (re-ID) to resolve the fragmented tracklets. However, simply using appearance information could be insufficient for videos containing severe pose changes, such as sp
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Federated Classification With Low Complexity Reproducing Kernel Hilbert Space Representations
In federated learning, a centralized model is realized based on information received from a group of agents each collecting data. This setting has two major challenges: the agents observe data over different distributions and they have only limited capabi
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Dynamic Attack Scoring Using Distributed Local Detectors
Nowadays, continuously operating critical services increasingly rely on complex cyber-physical systems, which are also known as high-profile targets of cyberattacks, potentially resulting in security breaches that can cause severe damage. This paper prese
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F0-Consistent Many-To-Many Non-Parallel Voice Conversion Via Conditional Autoencoder
Non-parallel many-to-many voice conversion remains an interesting but challenging speech processing task. Many style-transfer-inspired methods such as generative adversarial networks (GANs) and variational autoencoders (VAEs) has been proposed. Recently,
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Sequence-To-Sequence Automatic Speech Recognition With Word Embedding Regularization And Fused Decoding
In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduced the word embedding regularization by maximizing the cosine similarity be
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Gated Attentive Convolutional Network Dialogue State Tracker
In task-oriented dialogue systems, dialogue state tracking (DST) is an essential part which aims to estimate user goal at every step of the dialogue. At each turn, DST aims to estimate user goals by current user utterance and last system action. However,
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Kernel Computations From Large-Scale Random Features Obtained By Optical Processing Units
Approximating kernel functions with random features (RFs) has been a successful application of random projections for nonparametric estimation. However, performing random projections presents computational challenges for large-scale problems. Recently, a
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Sound Event Localization Based On Sound Intensity Vector Refined By Dnn-Based Denoising And Source Separation
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. completely-data-driven approach, achieves high accuracy. However, there is a gap
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Sight To Sound: An End-To-End Approach For Visual Piano Transcription
Automatic music transcription has primarily focused on transcribing audio to a symbolic music representation (e.g. MIDI or sheet music). However, audio-only approaches often struggle with polyphonic instruments and background noise. In contrast, visual in
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Crowdsourcing-Based Ranking Aggregation For Person Re-Identification
Person re-identification (re-ID) is widely applied in surveillance and criminal detection applications. The existing research focus on devising the stand-alone re-ID methods, ignoring their practical application in the multi-person collaboration scenario.
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Voice Conversion With Transformer Network
This paper describes an end-to-end voice conversion system, which involves three main ideas: transformer, context preservation mechanisms, and model adaptation. Self-attention in the transformer architecture directly connects all positions, making it easi
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Masking And Inpainting: A Two-Stage Speech Enhancement Approach For Low Snr And Non-Stationary Noise
Currently, low signal-to-noise ratio (SNR) and non-stationary noise cause severe performance degradation for most of speech enhancement models. For better speech enhancement at the above scenarios, this paper proposes a two-stage approach that consists of
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How Much Self-Attention Do We Need? Trading Attention For Feed-Forward Layers
We propose simple architectural modifications in the standard Transformer with the goal to reduce its total state size (defined as the number of self-attention layers times the sum of the key and value dimensions, times position) without loss of performan
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Empirical Sure-Guided Microscopy Super-Resolution Image Reconstruction From Confocal Multi-Array Detectors
The new generation of confocal microscopes are equipped with an array detector that generates an array of images corresponding to a multiview of the same sample. Several computational methods have been proposed to reconstruct a single super-resolution ima
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Artificial Bandwidth Extension Using Conditional Variational Auto-Encoders And Adversarial Learning
Artificial bandwidth extension (ABE) algorithms have been developed to estimate missing highband frequency components (4-8kHz) to improve quality of narrowband (0-4kHz) telephone calls. Most ABE solutions employ deep neural networks (DNNs) due to their we
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Global Structure Graph Guided Fine-Grained Vehicle Recognition
Fine-grained vehicle recognition is a challenging problem due to the subtle intra-category appearance variation, which requires the recognition model can capture discriminative features from distinguishing regions. The structure is an important characteri