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The Fifthnet Chroma Extractor
Deep Learning (DL) is now commonly used in music processing such as Automatic Chord Recognition (ACR), with Convolutional Neural Networks (CNN) being popular in such tasks. Compression of CNNs has become a research topic of interest, focussed on post-prun
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Projected Weight Regularization To Improve Neural Network Generalization
Generalization of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new technique, named projected weight regularization (PWR), to improve the generalizati
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An Acoustic Modelling Based Remote Error Sensing Approach For Quiet Zone Generation In A Noisy Environment
Remote error sensing is required in active noise control systems when they are used to create a quiet zone in a noisy environment with the constraint that the error microphones cannot be inside the zone. The challenge in remote error sensing is to estimat
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Performance Analysis And Constellation Optimization Of Star-Qam-Aided Differential Faster-Than-Nyquist Signaling
In this letter, motivated by the recent differential faster-than-Nyquist (DFTN) signaling concept, we propose an improved 16-point double-ring star quadrature amplitude modulation (QAM)-aided DFTN signaling transmission, which allows us to attain a higher
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Direction Of Arrival Estimation For Reverberant Speech Based On Enhanced Decomposition Of The Direct Sound
Direction of arrival (DOA) estimation for speech sources is an important task in audio signal processing. This task becomes a challenge in reverberant environments, which are typical to real scenarios. Several DOA estimation methods for speech sources hav
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Realistic Real-Time Voice Swapping From Single Unpaired Sentences
We demonstrate a system that allows two speakers to swap their voices from any two unpaired sentences such that the result is indistinguishable from real voices and performed in real-time on a laptop. Each of the two speakers takes turns pronouncing any u
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Machine Learning-Based Adaptive Receive Filtering: Proof-Of-Concept On An Sdr Platform
The constant demand for low latency and high data rates in a modern mobile communications network creates new scientific challenges in each new generation. An accurate reconstruction of transmission data of as many users as possible at the base station is
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High-Accuracy Classification Of Attention Deficit Hyperactivity Disorder With L2,1-Norm Linear Discriminant Analysis
Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological classification is meaningful for clinicians. The existing ADHD classification methods suffer from two problems, i.e.
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Bilateral Recurrent Network For Single Image Deraining
Single image deraining has been widely studied in recent years. Motivated by residual learning, most deep learning based deraining approaches devote research attention to extracting rain streaks, usually yielding visual artifacts in final deraining images
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Semantic Augmentation Hashing For Zero-Shot Image Retrieval
Hashing technique has been widely applied to large-scale image retrieval due to its efficacy in storage and retrieval. However, due to the explosive growth of multimedia data on the web, existing hashing approaches can hardly achieve satisfactory performa
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Diversity And Sparsity: A New Perspective On Index Tracking
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must a
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Lightweight V-Net For Liver Segmentation
The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To addr
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Depth Estimation From Single Image Through Multi-Path-Multi-Rate Diverse Feature Extractor
Convolutional neural networks can effectively learn features and predict the depth by considering different scene types. However, previous studies have not accurately predicted the depth in cases wherein the objects or scenes were small and the background
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Discriminant Generative Adversarial Networks With Its Application To Equipment Health Classification
In equipment health classification, machines in normal, degradation and critical stages are classified based on domain experts KPI (Remaining Useful Life). Higher KPI values indicate healthier machines. GANs can be used to generate sensor data for machine
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Position Constraint Loss For Fashion Landmark Estimation
Fashion landmark estimation aims at locating functional key points of clothes, which has wide potential applications in electronic commerce. However, due to the occlusion and weak outline information, landmark estimation occurs outliers and duplicate dete
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Efficient Scene Text Detection With Textual Attention Tower
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion
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Snorer Diarisation Based On Deep Neural Network Embeddings
Acoustic analysis of sleep breathing sounds using a smartphone at home provides a much less obtrusive means of screening for sleep-disordered breathing (SDB) than assessment in a sleep clinic. However, application in a home environment is confounded by th
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Robust Full-Fov Depth Estimation In Tele-Wide Camera System
Tele-wide camera system with different Field of View (FoV) lenses becomes very popular in recent mobile devices. Usually it is difficult to obtain full-FoV depth based on traditional stereo-matching methods. Pure Deep Neural Network (DNN) based depth esti
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Learning Task-Based Analog-To-Digital Conversion For Mimo Receivers
Analog-to-digital conversion allows physical signals to be processed using digital hardware. This conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-ampl
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Scene Text Recognition With Temporal Convolutional Encoder
Texts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequence models by an encoder to have the visual representations and then a decode
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Enhancing The Labelling Of Audio Samples For Automatic Instrument Classification Based On Neural Networks
The polyphonic OpenMIC-2018 dataset is based on weak and incomplete labels. The automatic classification of sound events, based on the VGGish bottleneck layer as proposed before by the AudioSet, implies the classification of only one second at a time, mak
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Robust Symbol-Level Precoding Via Autoencoder-Based Deep Learning
This paper proposes an autoencoder-based symbol-level precoding (SLP) scheme for a massive multiple-input multiple-output (MIMO) system operating in a limited-scattering environment. By recognizing that only imperfect channel state information (CSI) is av
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What Did Your Adversary Believe? Optimal Filtering And Smoothing In Counter-Adversarial Autonomous Systems
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our current state via a noisy sensor, ii) computes a posterior estimate (belief) and
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Rgb-D Based Multi-Modal Deep Learning For Face Identification
In recent years, the rapid development of depth cameras and wide application scenarios. The depth image information becomes more influential in face identification. In the proposed architecture, we implement the networks in dual CNN paths for color and de
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Fddwnet: A Lightweight Convolutional Neural Network For Real-Time Semantic Segmentation
This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweightnetworks that prefer to utilize shallow structure, FDDWNet makes an effort to desi
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Performance Study Of A Convolutional Time-Domain Audio Separation Network For Real-Time Speech Denoising
Time-domain audio separation networks based on dilated temporal convolutions have recently been shown to perform very well compared to methods that are based on a time-frequency representation in speech separation tasks, even outperforming an oracle binar
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Source Separation With Weakly Labelled Data: An Approach To Computational Auditory Scene Analysis
Source separation is the task of separating an audio recording into individual sound sources. Source separation is fundamental for computational auditory scene analysis. Previous work on source separation has focused on separating particular sound classes
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A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality And Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In t
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Graph Neural Net Using Analytical Graph Filters And Topology Optimization For Image Denoising
While convolutional neural nets (CNNs) have achieved remarkable performance for a wide range of inverse imaging applications, the filter coefficients are computed in a purely data-driven manner and are not explainable. Inspired by an analytically derived
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Scalable Multilingual Frontend For Tts
This paper describes progress towards making a Neural Text-to-Speech (TTS) Frontend that works for many languages and can be easily extended to new languages. We take a Machine Translation (MT) inspired approach to constructing the frontend, and model bot
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Fast Training Of Deep Neural Networks For Speech Recognition
Training large, deep neural network acoustic models for speech recognition on large datasets takes a long time on a single GPU, motivating research on parallel training algorithms. We present an approach for training a bidirectional LSTM acoustic model on
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Time-Frequency Feature Decomposition Based On Sound Duration For Acoustic Scene Classification
Acoustic scene classification is the task of identifying the type of acoustic environment in which a given audio signal is recorded. The signal is a mixture of sound events with various characteristics. In-depth and focused analysis is needed to find out
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Hierarchical Caching Via Deep Reinforcement Learning
Next generation wireless and wireline networks, including Internet, cellular, and content delivery networks are to serve user file requests proactively. To this aim, by storing anticipated popular contents during off-peak periods, and fetching them to end
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A Variational Bayesian Approach For Multichannel Through-Wall Radar Imaging With Low-Rank And Sparse Priors
This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the radar signals observed along several channels, a joint distribution of the observed data and latent variables is formula
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Corrdrop: Correlation Based Dropout For Convolutional Neural Networks
Convolutional neural networks (CNNs) can be easily over-fitted when they are over-parametered. The popular dropout that drops feature units randomly can't always work well for CNNs, due to the problem of under-dropping. To eliminate this problem, some str
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Complex Trainable Ista For Linear And Nonlinear Inverse Problems
Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications. In this paper, we propose a trainable iterative signal recovery algorithm named complex-field TISTA (C-T
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Greedy Hybrid Rate Adaptation In Dynamic Wireless Communication Environment
High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/ne
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On Exponentially Consistency Of Linkage-Based Hierarchical Clustering Algorithm Using Kolmogrov-Smirnov Distance
This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distributions. Th
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Unsupervised Speaker Adaptation Using Attention-Based Speaker Memory For End-To-End Asr
We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a memory block that holds speaker i-vectors extracted from the training data an
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A Dialogical Emotion Decoder For Speech Emotion Recognition In Spoken Dialog
Developing a robust emotion speech recognition (SER) system for human dialog is important in advancing conversational agent design. In this paper, we proposed a novel inference algorithm, a dialogical emotion decoding (DED) algorithm, that treats a dialog
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Reliable And Secure Transmission For Future Networks
This paper introduces a novel physical layer encryption method called randomized reciprocal channel modulation (RRCM) for reliable and secure transmission of information against eavesdropper (Eve) with any number of antennas and any noise level. RRCM make
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Cross-Domain Joint Dictionary Learning For Ecg Reconstruction From Ppg
An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventiv
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Learning With Out-Of-Distribution Data For Audio Classification
In supervised machine learning, the standard assumptions of data and label integrity are not always satisfied due to cost constraints or otherwise. In this paper, we investigate a case of this for classification tasks in which the dataset is corrupted wit
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Node-Asynchronous Spectral Clustering On Directed Graphs
In recent years the convergence behavior of random node asynchronous graph communications have been studied for the case of undirected graphs. This paper extends these results to the case of graphs having arbitrary directed edges possibly with a non-diago
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Lance: Efficient Low-Precision Quantized Winograd Convolution For Neural Networks Based On Graphics Processing Units
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quantized Winograd convolution algorithm, called LANCE, which combines the ad
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Automatic Prediction Of Suicidal Risk In Military Couples Using Multimodal Interaction Cues From Couples Conversations
Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, w
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Adaptive Elastic Loss Based On Progressive Inter-Class Association For Cervical Histology Image Segmentation
Cervical cancer is one of the most commonly diagnosed cancer types worldwide, while is curable if detected early. However, few computer-aided algorithms have been explored on cervical histology image, which is vital for abnormality assessment. In this pap
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Augmented Grad-Cam: Heat-Maps Super Resolution Through Augmentation
We present Augmented Grad-CAM, a general framework to provide a high-resolution visual explanation of CNN outputs. Our idea is to take advantage of image augmentation to aggregate multiple low-resolution heat-maps -- in our experiments Grad-CAMs -- comput