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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
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Fast Start-Up Algorithm For Adaptive Noise Cancellers With Novel Snr Estimation And Stepsize Control
This paper proposes a fast convergence algorithm for adaptive noise cancellers with novel SNR (signal-to-noise ratio) estimation and stepsize control. The stepsize for coefficient adaptation is controlled with an estimated SNR for low distortion of the ou
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Defending Graph Convolutional Networks Against Adversarial Attacks
The interconnection of social, email, and media platforms enables adversaries to manipulate networked data and promote their malicious intents. This paper introduces graph neural network architectures that are robust to perturbed networked data. The novel
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Improving Universal Sound Separation Using Sound Classification
Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification. Most audio source separation approaches focus only on separating sources belonging to a restricted domain of source classes, s
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Private Fl-Gan: Differential Privacy Synthetic Data Generation Based On Federated Learning
Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic ``fake'' data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN's training is difficult. To addr
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Gfcn: A New Graph Convolutional Network Based On Parallel Flows
In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory. In
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Towards Fast And Accurate Streaming End-To-End Asr
End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parameters than a conventional ASR system, thus making it suitable for on-device
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Dnn-Based Mask Estimation Integrating Spectral And Spatial Features For Robust Beamforming
Spectral mask based beamforming has showed competitive performance on multi-channel speech enhancement in recent years. However, such methods apply mask estimation on each channel and ensemble the masks from multiple channels into one for speech and noise
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Pitchnet: Unsupervised Singing Voice Conversion With Pitch Adversarial Network
Singing voice conversion is to convert a singer's voice to another one's voice without changing singing content. Recent work shows that unsupervised singing voice conversion can be achieved with an autoencoder-based approach cite{nachmani2019unsupervised
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A Sparse Linear Array Approach In Automotive Radars Using Matrix Completion
We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA be
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End-End Speech-To-Text Translation With Modality Agnostic Meta-Learning
Collecting large amounts of data to train end-to-end Speech Translation (ST) models is more difficult compared to the ASR and MT tasks. Previous studies have proposed the use of transfer learning approaches to overcome the above difficulty. These approach
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Distributed Verification Of Belief Precisions Convergence In Gaussian Belief Propagation
Gaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions
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Array-Geometry-Aware Spatial Active Noise Control Based On Direction-Of-Arrival Weighting
Active noise control (ANC) over a sizeable space ideally requires uniformly distributed sensors and secondary sources, which limits the feasibility of practically realizing such systems. In this paper, we propose a direction of arrival (DOA) weighting alg
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Static Visual Spatial Priors For Doa Estimation
As we interact with the world, for example when we communicate with our colleagues in a large open space or meeting room, we continuously analyse the surrounding environment and, in particular, localise and recognise acoustic events. While we largely take