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Sound Texture Synthesis Using Ri Spectrograms
This article introduces a new parametric synthesis method for sound textures based on existing works in visual and sound texture synthesis. Starting from a base sound signal, an optimization process is performed until the cross-correlations between the fe
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Fully Pipelined Iteration Unrolled Decoders The Road To Tb/S Turbo Decoding
Turbo codes are a well-known code class used for example in the LTE mobile communications standard. They provide built-in rate flexibility and a low-complexity and fast encoding. However, the serial nature of their decoding algorithm makes high-throughput
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Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling
We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss func
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Joint Coding And Modulation In The Ultra-Short Blocklength Regime For Bernoulli-Gaussian Impulsive Noise Channels Using Autoencoders
This paper develops a joint coding and modulation scheme for end-to-end communication system design using an autoencoder architecture in the ultra-short blocklength regime. Unlike the classical approach of separately designing error correction codes and m
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Leveraging Unpaired Text Data For Training End-To-End Speech-To-Intent Systems
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is time consuming and expensive to collect. Initializing the S2I model with an
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Active Learning With Unsupervised Ensembles Of Classifiers
The present work introduces a simple scheme for active classification of data using unsupervised ensembles of classifiers. Uncertainty sampling, with different uncertainty measures, is evaluated for data selection, while an online expectation maximization
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Maximum Likelihood Multi-Speaker Direction Of Arrival Estimation Utilizing A Weighted Histogram
In this contribution, a novel maximum likelihood (ML) based direction of arrival (DOA) estimator for concurrent speakers in a noisy reverberant environment is presented. The DOA estimation task is formulated in the short-time Fourier transform (STFT) in t
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Optimal Joint Channel Estimation And Data Detection By L1-Norm Pca For Streetscape Iot
We prove, for the first time in the literature of communication theory and machine learning, the equivalence of joint maximum-likelihood (ML) optimal channel estimation and data detection (JOCEDD) to the problem of finding the $L_1$-norm principal compone
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Quickest Detection Of Growing Dynamic Anomalies In Networks
The problem of quickest growing dynamic anomaly detection in sensor networks is studied. Initially, the observations at the sensors, which are sampled sequentially by the decision maker, are generated according to a pre-change distribution. At some unknow
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Mt-Gcn For Multi-Label Audio Tagging With Noisy Labels
Multi-label audio tagging is the task of predicting the types of sounds occurring in an audio clip. Recently, large-scale audio datasets such as Google's AudioSet, have allowed researchers to use deep learning techniques for this task but this comes at th
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Small-Footprint Keyword Spotting On Raw Audio Data With Sinc-Convolutions
Keyword Spotting (KWS) enables speech-based user interaction on smart devices. Always-on and battery-powered application scenarios for smart devices put constraints on hardware resources and power consumption, while also demanding high accuracy as well as
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Far-Field Location Guided Target Speech Extraction Using End-To-End Speech Recognition Objectives
Target speech extraction is a specific case of source separation where an auxiliary information like the location or some pre-saved anchor speech examples of the target speaker is used to resolve the permutation ambiguity. Traditionally such systems are o
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A Single-Rf Architecture For Multiuser Massive Mimo Via Reflecting Surfaces
In this work, we propose a new single-RF MIMO architecture which enjoys high scalability and energy-efficiency. The transmitter in this proposal consists of a single RF illuminator radiating towards a reflecting surface. Each element on the reflecting sur
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Speech-To-Singing Conversion In An Encoder-Decoder Framework
In this paper our goal is to convert a set of spoken lines into sung ones. Unlike previous signal processing based methods, we take a learning based approach to the problem. This allows us to automatically model various aspects of this transformation, thu
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Sparse Low-Redundancy Linear Array With Uniform Sum Co-Array
Sparse arrays can resolve vastly more scatterers than the number of sensors, in tasks such as coherent source localization. This entails significant cost reductions compared to conventional arrays with uniformly spaced elements. In this paper, we introduc
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Sdtcn: Similarity Driven Transmission Computing Network For Image Dehazing
Transmission similarity is an important feature which can greatly increase the capability of convolutional neural network (CNN) to fit transmission map. However, it is not sufficiently utilized in existing algorithms. In this paper, we propose a novel lig
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One-Bit Compressed Sensing Using Generative Models
In this paper, we address the classical problem of one-bit compressed sensing. We present a deep learning based reconstruction algorithm that relies on a generative model. The generator which is a neural network, learns a mapping from a low dimensional sp
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Two-Element Biomimetic Antenna Array Design And Performance
Arrays of closely-spaced antennas with mutual coupling have been considered recently with analogies to the hearing mechanism in small insects that exhibit excellent direction finding capabilities. We develop a model for a two-element array system that inc
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Adaptive Region Aggregation Network: Unsupervised Domain Adaptation With Adversarial Training For Ecg Delineation
Electrocardiogram (ECG) delineation, which provides clinically useful information for the diagnosis of cardiovascular disease, is an essential task in automated ECG analysis. The discrepancies among ECG signals from different datasets, namely domain shift
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Multi-Microphone Complex Spectral Mapping For Speech Dereverberation
This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sou
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Mmse-Based Channel Estimation For Hybrid Beamforming Massive Mimo With Correlated Channels
In this paper, we study the channel estimation problem in microwave correlated massive multiple-input-multiple-output systems with reduced number of radio-frequency chains. We exploit the knowledge of the transmit and receive correlation between the anten
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Matching Pursuit Based Dynamic Phase-Amplitude Coupling Measure
Long-distance neuronal communication in the brain is enabled by the interactions across various oscillatory frequencies. One interaction that is gaining importance during cognitive brain functions is phase amplitude coupling (PAC), where the phase of a sl
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Improving Convergent Cross Mapping For Causal Discovery With Gaussian Processes
Convergent cross mapping (CCM) is designed for causal discovery between coupled time series for which Granger's method for detecting causality is shown to be unreliable. The theoretical foundation of CCM is based on state space reconstruction, and therefo
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Bio-Mimetic Attentional Feedback In Music Source Separation
Attention plays a vital role in sifting through the cacophony of sounds in everyday scenes by emphasizing the representation of targets sounds relative to distractors. While its conceptual role is well established, there are competing theories as to how a
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The Sound Of My Voice: Speaker Representation Loss For Target Voice Separation
Content and style representations have been widely studied in the field of style transfer. In this paper, we propose a new loss function using speaker content representation for audio source separation, and we call it speaker representation loss. The obje
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From Unsupervised Machine Translation To Adversarial Text Generation
We present a self-attention based bilingual adversarial text generator (B-GAN) which can learn to generate text from the encoder representation of an unsupervised neural machine translation system. B-GAN is able to generate a distributed latent space repr
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Space Filling Curves For Mri Sampling
A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs b
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Beamforming Design For High-Resolution Low-Intensity Focused Ultrasound Neuromodulation
Low-intensity focused ultrasound (LIFU) has been shown to modulate neural activity. Recent experiments suggest potential applications of LIFU stimulation for treating neuropsychiatric disorders like depression and Alzheimer's. The modulation effect is usu
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State-Space Gaussian Process For Drift Estimation In Stochastic Differential Equations
This paper is concerned with the estimation of unknown drift functions of stochastic differential equations (SDEs) from observations of their sample paths. We propose to formulate this as a non-parametric Gaussian process regression problem and use an It?
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A Multi-Dilation And Multi-Resolution Fully Convolutional Network For Singing Melody Extraction
Each human cognitive function involves bottom-up and top-down processes. Several methods have been proposed for singing melody extraction by emphasizing either the bottom-up or top-down processes. For hearing, the bottom-up processes include spectral and
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Height And Weight Estimation From Unconstrained Images
We address the difficult problem of estimating the weight and height of individuals from pictures taken in completely unconstrained settings. We present a deep learning scheme that relies on simultaneous prediction of human silhouettes and skeletal joints
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A Novel Pruning Approach For Bagging Ensemble Regression Based On Sparse Representation
This work aims to propose an approach for pruning a bagging ensemble regression (BER) model based on sparse representation, which we call sparse representation pruning (SRP). Firstly, a BER model with a specific number of subensembles should be trained. T
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Deriving Compact Feature Representations Via Annealed Contraction
It is common practice to use pretrained image recognition models to compute feature representations for the visual data. The size of the feature representations can have a noticeable impact on the complexity of the models that use these representations, a
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Graph Auto-Encoder For Graph Signal Denoising
Signal denoising is an important problem with a vast literature. Recently, signal denoising on graphs has received a lot of attention due to the increasing use of graph-structured signals. However, well-etablished signal denoising methods do not generaliz
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Time-Domain Neural Network Approach For Speech Bandwidth Extension
In this paper, we study the time-domain neural network approach for speech bandwidth extension. We propose a network architecture, named multi-scale fusion neural network (MfNet), that gradually restores the low-frequency signal and predicts the high-freq
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Exploiting Two-Dimensional Symmetry And Unimodality For Model-Free Source Localization In Harsh Environment
Knowing the location of a transceiver may enable advanced radio resource management strategies in sensing and communication networks. However, there are many scenarios where users operate in a non-cooperative mode with no localization-dedicated signaling
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Multi-Head Attention For Speech Emotion Recognition With Auxiliary Learning Of Gender Recognition
The paper presents a Multi-Head Attention deep learning network for Speech Emotion Recognition (SER) using Log mel-Filter Bank Energies (LFBE) spectral features as the input. The multi-head attention along with the position embedding jointly attends to in
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Generalized Graph Spectral Sampling With Stochastic Priors
We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and as
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View-Angle Invariant Object Monitoring Without Image Registration
Object monitoring can be performed by change detection algorithms. However, for the image pair with a large perspective difference, the change detection performance is usually impacted by inaccurate image registration. To address the above difficulties, a
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Spoken Language Acquisition Based On Reinforcement Learning And Word Unit Segmentation
The process of spoken language acquisition has been one of the topics which attract the greatest interesting from linguists for decades. By utilizing modern machine learning techniques, we simulated this process on computers, which helps to understand the
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Investigation Of Methods To Improve The Recognition Performance Of Tamil-English Code-Switched Data In Transformer Framework
Code-switching (CS) refers to (inter/intra-word) switching between multiple languages in a single conversation. In multilingual countries like India, CS occurs very often in everyday speech, resulting in a new breed of languages in urban regions like Hing
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Video Deblurring Via 3D Cnn And Fourier Accumulation Learning
Camera shake and target movement often leads to undesirable image blurring in videos. How to exploit spatial-temporal information of adjacent frames and reduce the processing time of deblurring are two major issues in video deblurring. In this paper, we p
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Hydranet: A Real-Time Waveform Separation Network
Real-time source separation has become increasingly important, as more and more applications, such as voice recognition and voice commands, require clean audio input in noisy environments. Recent developments in deep learning have allowed models to direct
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Computability Of The Peak Value Of Bandlimited Signals
In this paper we study the peak value problem, i.e., the task of computing the peak value of a bandlimited signal from its samples. The peak value problem is important, for example, in communications, where the peak value of the transmit signal has to be
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Meta-Learning Extractors For Music Source Separation
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for i
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Unsupervised Variational Bayesian Kalman Filtering For Large-Dimensional Gaussian Systems
This paper considers the unsupervised filtering problem for large-dimensional linear and Gaussian systems, a setup in which the optimal Kalman filter (KF) might not be usable due to the exorbitant computational cost and storage requirements. For this prob
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Cross-Stained Segmentation From Renal Biopsy Images Using Multi-Level Adversarial Learning
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in different types of stained datasets due to the appearance variations. In th
