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Epigraphical Reformulation For Non-Proximable Mixed Norms
This paper proposes an epigraphical reformulation (ER) technique for non-proximable mixed norm regularization. Various regularization methods using "mixed norms" have been proposed, where their optimization relies on efficient computation of the proximity
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A Study Of Child Speech Extraction Using Joint Speech Enhancement And Separation In Realistic Conditions
In this paper, we design a novel joint framework of speech enhancement and speech separation for child speech extraction in realistic conditions, targeting the problem of extracting child speech from daily conversations in BabyTrain mega corpus. To the be
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Back-And-Forth Prediction For Deep Tensor Compression
Recent AI applications such as Collaborative Intelligence with neural networks involve transferring deep feature tensors between various computing devices. This necessitates tensor compression in order to optimize the usage of bandwidth-constrained channe
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Transfer Learning From Youtube Soundtracks To Tag Arctic Ecoacoustic Recordings
Sound provides a valuable tool for long-term monitoring of sensitive animal habitats at a spatial scale larger than camera traps or field observations, while also providing more details than satellite imagery. Currently, the ability to collect such record
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Preconditioned Ghost Imaging Via Sparsity Constraint
Ghost imaging via sparsity constraint (GISC) can recover objects from the intensity fluctuation of light fields at a sampling rate far below the Nyquist rate. However, its imaging quality may degrade severely when the coherence of sampling matrices is lar
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Learning-Based Content Caching And User Clustering: A Deep Deterministic Policy Gradient Approach
The joint design of content caching and user clustering (JCC) in cache-enabled heterogeneous networks is challenging, due to various unknown, possibly time-varying, system parameters which potentially give rise to various design tradeoffs in practice. Thi
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Upgrading Crfs To Jrfs And Its Benefits To Sequence Modeling And Labeling
Two important sequence tasks are sequence modeling and labeling. Sequence modeling involves determining the probabilities of sequences, e.g. language modeling. It is still difficult to improve language modeling with additional relevant tags, e.g. part-of-
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Accent Estimation Of Japanese Words From Their Surfaces And Romanizations For Building Large Vocabulary Accent Dictionaries
In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence. However, there are a limited number of publicly available accent dictionaries, and those dictionaries e.g. UniDic, do not contain many compound words, prope
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Communication Constrained Learning With Uncertain Models
We consider the problem of distributed inference of a group of agents in a social network, where the agents construct, share, and update beliefs in a non-Bayesian framework to identify the underlying true state of the world. We build upon the concept of u
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Leveraging Ordinal Regression With Soft Labels For 3D Head Pose Estimation From Point Sets
Head pose estimation from depth image is a challenging problem, considering its large pose variations, severer occlusions, and low quality of depth data. In contrast to existing approaches that take 2D depth image as input, we propose a novel deep regress
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Optimal Window Design For W-Ofdm
Windowing is an effective approach to reduce out-of-band radiation (OBR) in multicarrier systems in order to avoid adjacent channel interference. However, commonly used window functions are chosen in an ad hoc manner and fixed. We present an optimal windo
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Multi-Motifgan (Mmgan): Motif-Targeted Graph Generation And Prediction
Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity pattern
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A Study Of Generalization Of Stochastic Mirror Descent Algorithms On Overparameterized Nonlinear Models
We study the convergence, the implicit regularization and the generalization of stochastic mirror descent (SMD) algorithms in overparameterized nonlinear models, where the number of model parameters exceeds the number of training data points. Due to overp
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Nonlinear Spatial Filtering For Multichannel Speech Enhancement In Inhomogeneous Noise Fields
A common processing pipeline for multichannel speech enhancement is to combine a linear spatial filter with a single-channel postfilter. In fact, it can be shown that such a combination is optimal in the minimum mean square error (MMSE) sense if the noise
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A New Application Of Ultrasound Signal Processing For Archaeological Ceramic Classification
Identifying archaeological ceramic pieces is a challenging problem for archaeologists, since fragments of archaeological pottery from the same site might have been made in different distant locations from the site. The pieces look very similar and context
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One-Bit Doa Estimation Via Sparse Linear Arrays
Parameter estimation from noisy and quantized received signals has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. Techniques to achieve high estimation performance in spite of the coarse qua
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Fast Optical System Identification By Numerical Interferometry
We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse problem
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A Deep Learning Architecture For Epileptic Seizure Classification Based On Object And Action Recognition
Epilepsy affects approximately 1% of the world’s population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to c
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Hybrid Neural-Parametric F0 Model For Singing Synthesis
We propose a novel hybrid neural-parametric fundamental frequency generation model for singing voice synthesis. A recurrent neural network predicts the parameters of a flexible parametric F0 model, conditioned on a given input score. Rather than trying to
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Automatic Classification Of Volumes Of Water Using Swallow Sounds From Cervical Auscultation
The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can chara
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Anomalous Sound Detection Based On Interpolation Deep Neural Network
As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoenc
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Localized Linear Regression In Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (gene
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Connections Between Spectral Properties Of Asymptotic Mappings And Solutions To Wireless Network Problems
We establish connections between asymptotic functions and properties of solutions to problems in wireless networks. We start by introducing self-mappings (called asymptotic mappings) constructed with asymptotic functions, and we show that their spectral p
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Super-Resolution Via Image-Adapted Denoising Cnns: Incorporating External And Internal Learning
While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mis
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Learning Based Reconfigurable Wideband Non-Contiguous Spectrum Characterization For 5G Applications
Introduction of spectrum sharing in 3GPP Release 17 demands base-stations (BS) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Since, multiple-antenna and beam-forming
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Convolutional Beamspace For Array Signal Processing
A new type of beamspace for array processing is introduced called convolutional beamspace. It enjoys the advantages of traditional beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution thres
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Automatic Lyrics Alignment And Transcription In Polyphonic Music: Does Background Music Help?
Automatic lyrics alignment and transcription in polyphonic music are challenging tasks because the singing vocals are corrupted by the background music. In this work, we propose to learn music genre-specific characteristics to train polyphonic acoustic mo
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Data-Driven Model Set Design For Model Averaged Particle Filter
This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major adv
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Robust Multi-Channel Speech Recognition Using Frequency Aligned Network
Conventional speech enhancement technique such as beamforming has known benefits for far-field speech recognition. Our own work in frequency-domain multi-channel acoustic modeling has shown additional improvements by training a spatial filtering layer joi
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C3Dvqa: Full-Reference Video Quality Assessment With 3D Convolutional Neural Network
Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality exhibits different characteristics from static image quality due to the exist
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Lightweight Hardware Implementation Of Vvc Transform Block For Asic Decoder
Versatile Video Coding (VVC) is the next generation video coding standard expected by the end of 2020. Compared to its predecessor, VVC introduces new coding tools and techniques to make compression more ef?cient at the expense of higher computational com
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Blood Pressure Estimation From Ppg Signals Using Convolutional Neural Networks And Siamese Network
Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two te
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Object Detection With Color And Depth Images With Multi-Reduced Region Proposal Network And Multi-Pooling
Object detection technology has received increasing research attention with recent developments in automation technology. Most studies in this field, however, use RGB images as input to deep-learning classifiers, and they rarely use depth information. So,
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Subject Transfer Framework Based On Source Selection And Semi-Supervised Style Transfer Mapping For Semg Pattern Recognition
To construct subject-specific feature extractors and classifiers for a new subject using pooled datasets, overcoming inter-subject variabilities is required. In this study, we investigate the efficiency of the proposed subject transfer framework, which ap
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Using Vaes And Normalizing Flows For One-Shot Text-To-Speech Synthesis Of Expressive Speech
We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a
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A Novel Two-Pathway Encoder-Decoder Network For 3D Face Reconstruction
3D Morphable Model(3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address th
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Mixup-Breakdown: A Consistency Training Method For Improving Generalization Of Speech Separation Models
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditions. To address this problem, we propose an easy-to-implement yet effective
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A Whiteness Test Based On The Spectral Measure Of Large Non-Hermitian Random Matrices
In the context of multivariate time series, a whiteness test against an MA(1) correlation model is proposed. This test is built on the eigenvalue distribution (spectral measure) of the non-Hermitian one-lag sample autocovariance matrix, instead of its sin
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Neural Coding Strategies For Event-Based Vision Data
Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Pre
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Online Channel Estimation For Hybrid Beamforming Architectures
Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) ar
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Tensor Decomposition-Based Beamspace Esprit Algorithm For Multidimensional Harmonic Retrieval
Beamspace processing is an efficient and commonly used approach in harmonic retrieval (HR). In the beamspace, measurements are obtained by linearly transforming the sensing data, thereby achieving a compromise between estimation accuracy and system comple
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Decidable Variable-Rate Dataflow For Heterogeneous Signal Processing Systems
Dynamic dataflow models of computation have become widely used through their adoption to popular programming frameworks such as TensorFlow and GNU Radio. Although dynamic dataflow models offer more programming freedom, they lack analyzability compared to
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A Neural Network For Monaural Intrusive Speech Intelligibility Prediction
Monaural intrusive speech intelligibility prediction (SIP) methods aim to predict the speech intelligibility (SI) of a single-microphone noisy and/or processed speech signal using the underlying clean speech signal. In the present work, we propose a neura
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Tracing Network Evolution Using The Parafac2 Model
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is
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Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense Mmwave Networks
The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communicati
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Detection Of Malicious Vbscript Using Static And Dynamic Analysis With Recurrent Deep Learning
Attackers have used malicious VBScripts as an important computer infection vector. In this study, we explore a system that employs both static and dynamic analysis to detect malicious VBScripts. For the static analysis, we investigate two deep recurrent m
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Media Classification With Bayesian Optimization And Vapnik-Chervonenkis (Vc) Bounds
The automatic classification of content is an essential requirement for multimedia applications. Present research for audio-based classifiers uses short- and long-term analysis of signals, with temporal and spectral features. In our prior study, we presen