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Graph Regularized Tensor Train Decomposition
With the advances in data acquisition technology, tensor objects are collected in a variety of applications including multimedia, medical and hyperspectral imaging. As the dimensionality of tensor objects is usually very high, dimensionality reduction is
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Determined Source Separation Using The Sparsity Of Impulse Responses
In this paper, we propose an over-determined sound source separation method considering the sparsity of impulse responses. Conventional methods, including independent low-rank matrix analysis (ILRMA), have mainly focused on design of realistic sound gener
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Noise-Robust Key-Phrase Detectors For Automated Classroom Feedback
With the goal of giving teachers automated feedback about their classrooms, we investigate how to train automatic speech detectors of key phrases such as good job, thank you, please, and you're welcome. This kind of language conveys support and respect fr
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Learning To Separate Sounds From Weakly Labeled Scenes
Deep learning models for monaural audio source separation are typically trained on large collections of isolated sources, which may not be available in domains such as environmental monitoring. We propose objective functions and network architectures that
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Optimizing Bayesian Hmm Based X-Vector Clustering For The Second Dihard Speech Diarization Challenge
This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input recording. In
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Exocentric To Egocentric Image Generation Via Parallel Generative Adversarial Network
Cross-view image generation has been recently proposed to generate images of one view from another dramatically different view. In this paper we investigate exocentric (third-person) view to egocentric (first-person) view image generation. This is a chall
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Whosecough: In-The-Wild Cougher Verification Using Multitask Learning
Current automatic cough counting systems can determine how many coughs are present in an audio recording. However, they cannot determine who produced the cough. This limits their usefulness as most systems are deployed in locations with multiple people (i
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Exact Sparse Nonnegative Least Squares
We propose a novel approach to solve exactly the sparse nonnegative least squares problem, under hard l0 sparsity constraints. This approach is based on a dedicated branch-and-bound algorithm. This simple strategy is able to compute the optimal solution e
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Converting Written Language To Spoken Language With Neural Machine Translation For Language Modeling
When building a language model (LM) for spontaneous speech, the ideal situation is to have a large amount of spoken, in-domain training data. Having such abundant data, however, is not realistic. We address this problem by generating texts in spoken langu
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Exploring Appropriate Acoustic And Language Modelling Choices For Continuous Dysarthric Speech Recognition
There has been much recent interest in building continuous speech recognition systems for people with severe speech impairments, e.g., dysarthria. However, the datasets that are commonly used are typically designed for tasks other than ASR development, or
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A Semi-Supervised Rank Tracking Algorithm For On-Line Unmixing Of Hyperspectral Images
This paper addresses the problem of rank tracking in real time hyperspectral image unmixing methods. Based on the On-line Alternating Direction Method of Multipliers (ADMM), we propose a new hyperspectral unmixing approach that integrates prior informatio
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Compressive Adaptive Bilateral Filtering
We propose a fast algorithm for an adaptive variant of the classical bilateral filter, where the range kernel is allowed to vary from pixel to pixel. Several fast and accurate algorithms have been proposed for bilateral filtering, but they assume that the
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Clock Synchronization Over Networks Using Sawtooth Models
Clock synchronization and ranging over a wireless network with low communication overhead is a challenging goal with tremendous impact. In this paper, we study the use of time-to-digital converters in wireless sensors, which provides clock synchronization
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Generating Empathetic Responses By Looking Ahead The User’S Sentiment
An important aspect of human conversation difficult for machines is conversing with empathy, which is to understand the user's emotion and respond appropriately. Recent neural conversation models that attempted to generate empathetic responses either focu
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Robust Pricing Mechanism For Resource Sustainability Under Privacy Constraint In Competitive Online Learning Multi-Agent Systems
We consider the problem of resource congestion control for competing online learning agents under privacy and security constraints. Based on the non-cooperative game as the model for agents' interaction and the noisy online mirror ascent as the model for
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Time Reversal Based Robust Gesture Recognition Using Wifi
Gesture recognition using wireless sensing opened a plethora of applications in the field of human-computer interaction. However, most existing works are not robust without requiring wearables or tedious training/calibration. In this work, we propose WiGR
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Improving Prosody With Linguistic And Bert Derived Features In Multi-Speaker Based Mandarin Chinese Neural Tts
Recent advances of neural TTS have made ?human parity? synthesized speech possible when a large amount of studio-quality training data from a voice talent is available. However, with only limited, casual recordings from an ordinary speaker, human-like TTS
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On Regularization Parameter For L0-Sparse Covariance Fitting Based Doa Estimation
In sparse DOA estimation methods, the regularization parameter is generally empirically tuned. In this paper, we provide a statistical method allowing to estimate an admissible interval where it must be chosen. This work is conducted in the case of an Uni
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Optimal Transport Based Change Point Detection And Time Series Segment Clustering
Two common problems in time series analysis are the decomposition of the data stream into disjoint segments, each of which is in some sense ?homogeneous? - a problem that is also referred to as Change Point Detection (CPD) - and the grouping of similar no
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Using Intelligent Reflecting Surfaces For Rank Improvement In Mimo Communications
An intelligent reflecting surface (IRS), consisting of reconfigurable metamaterials, can be used to partially control the radio environment and thereby bring new features to wireless communications. Previous works on IRS have particularly studied the rang
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Retinal Vessel Segmentation Via A Semantics And Multi-Scale Aggregation Network
Precise segmentation of retinal vessels is crucial for a computer-aided diagnosis system of retinal fundus images. However, this task remains challenging due to large variations in scales and poor segmentation of capillary vessels. In this paper, we propo
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Constrained Spectral Clustering For Dynamic Community Detection
Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the community structure, is an important problem in network science. Community
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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