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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
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Prediction Of Individual Progression Rate In Parkinson’S Disease Using Clinical Measures And Biomechanical Measures Of Gait And Postural Stability
Parkinson?s disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study
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Active Control Of Line Spectral Noise With Simultaneous Secondary Path Modeling Without Auxiliary Noise
Online secondary path modeling is appealing for most active noise control systems due to its benefit of effective tracking of the varying acoustic environment and possible variation of the control sources and sensors. However, the usually utilized additiv
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Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring
This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data j
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Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably And Efficiently
Multi-channel sparse blind deconvolution refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. It is challenging to learn the filter efficiently due to the bilinear structu
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Oov Recovery With Efficient 2Nd Pass Decoding And Open-Vocabulary Word-Level Rnnlm Rescoring For Hybrid Asr
In this paper, we investigate out-of-vocabulary (OOV) word recovery in word-based hybrid automatic speech recognition (ASR) systems, with emphasis on dynamic vocabulary expansion for both Weight Finite State Transducer (WFST)-based decoding and word-level
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Fast Clustering With Co-Clustering Via Discrete Non-Negative Matrix Factorization For Image Identification
How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is
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Apb2Face: Audio-Guided Face Reenactment With Auxiliary Pose And Blink Signals
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-re
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Predicting Performance Outcome With A Conversational Graph Convolutional Network For Small Group Interactions
Studying behaviors of members during small group interaction provides objective insights in improving the efficiency of the decision making process in our daily working life. By introducing the use of the graph structure in modeling the natural inter-memb
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Eliminating Out-Of-Cell Interference In Cellular Massive Mimo With A Single Additional Transceiver
Wireless cellular communication networks are bandwidth and interference limited. An important means to overcome these resource limitations is the use of multiple antennas. Base stations equipped with a very large (massive) number of antennas have been the
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Cpwc: Contextual Point Wise Convolution For Object Recognition
Convolutional layers are a major driving force behind the successes of deep learning. Pointwise convolution (PWC) is a 1x1 convolutional filter that is primarily used for parameter reduction. However, the PWC ignores the spatial information around the poi
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A Switching Transmission Game With Latency As The User's Communication Utility
We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. The user switches between two
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Distributed Wave-Domain Active Noise Control Based On The Diffusion Strategy
Conducting the spatial active noise control (ANC) in wave-domain has been shown advantageous over conventional point-based methods. In the existing schemes, signals at all error microphones are collected and processed in a centralized manner to update the
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Extrapolated Alternating Algorithms For Approximate Canonical Polyadic Decomposition
Tensor decompositions have become a central tool in machine learning to extract interpretable patterns from multiway arrays of data. However, computing the approximate Canonical Polyadic Decomposition (aCPD), one of the most important tensor decomposition