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An Alternative Signature Design Using L1 Principal Components For Spread-Spectrum Steganography
As methods for detecting hidden data evolve, there exits an ever increasing need to develop new steganographic solutions. This paper introduces novel spread spectrum (SS) and improved spread spectrum (ISS) multimedia data embedding techniques using L_1 pr
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Single Frequency Filter Bank Based Long-Term Average Spectra For Hypernasality Detection And Assessment In Cleft Lip And Palate Speech
Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing
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Fg2Seq: Effectively Encoding Knowledge For End-To-End Task-Oriented Dialog
End-to-end Task-oriented spoken dialog systems typically require modeling two types of inputs, namely, the dialog history which is a sequence of utterances and the knowledge base (KB) associated with the dialog history. While modeling these inputs, curren
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Tensorflow Audio Models In Essentia
Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexib
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Enhancing End-To-End Multi-Channel Speech Separation Via Spatial Feature Learning
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. However, these manually designed spatial features are hard to incorporate into
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Joint Beamforming And Reverberation Cancellation Using A Constrained Kalman Filter With Multichannel Linear Prediction
The performance of speech processing systems degrades significantly in far-field scenarios where the distance between the user and microphones increases, leading to low signal-to-noise and signal-to-reverberation ratios. To address this challenge, combini
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Trapezoidal Segment Sequencing: A Novel Approach For Fusion Of Human-Produced Continuous Annotations
Generating accurate ground truth representations of human subjective experiences and judgements is essential for advancing our understanding of human-centered constructs such as emotions. Often, this requires the collection and fusion of annotations from
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Classification Of Epileptic Ieeg Signals By Cnn And Data Augmentation
Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often co
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Efficient And Scalable Neural Residual Waveform Coding With Collaborative Quantization
Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corr
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Learning A Subword Inventory Jointly With End-To-End Automatic Speech Recogntion
Recent work has demonstrated the promise of using subword units as output targets for sequence-to-sequence automatic speech recognition (ASR) models. Our work builds on the latent sequence decomposition (LSD) framework, in which the use of subword units f
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Rate Assignment In 360-Degree Video Tiled Streaming Using Random Forest Regression
Streaming of high-resolution 360-degree video is typically done in a viewport-dependent fashion such as in the tile-based viewport-dependent profile of MPEG OMAF wherein clients continuously adapt their tile selection according to the user viewport. From
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Raw Waveform Based End-To-End Deep Convolutional Network For Spatial Localization Of Multiple Acoustic Sources
In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported deep learning based approaches work well in local
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Image Segmentation Based Privacy-Preserving Human Action Recognition For Anomaly Detection
Human Action Recognition and Anomaly Detection significantly improved automatic video analysis, assisted living, and video-based surveillance. The focus of this work is on those applications where privacy protection is required, such as surveillance and a
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A Multi-Scaled Receptive Field Learning Approach For Medical Image Segmentation
Biomedical image segmentation has been widely studied, and lots of methods have been proposed. Among these methods, attention U-Net has achieved a promising performance. However, it has drawbacks of extracting the multi-scaled receptive field features at
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In this work, we consider control of linear dynamical systems using sparse inputs. We provide an algorithm for determining a sequence of sparse inputs that will take the system from any given initial state to a desired final state, and stay in that state
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Study Of Formant Modification For Children Asr
The performance of automatic speech recognition systems for children?s speech is known to suffer from the large variation and mismatch in the acoustic and linguistic attributes between children?s and adults? speech. One of the various identified sources o
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Learning-Aided Content Placement In Caching-Enabled Fog Computing Systems Using Thompson Sampling
In this paper, we focus on the problem of online content placement with unknown content popularity in caching-enabled fog computing systems, i.e., how to decide and update cached content on resource-limited edge fog nodes to maximize cache hit rate and mi
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Epoch Extraction From A Speech Signal Using Gammatone Wavelets In A Scattering Network
In speech production, epochs are glottal closure instants where significant energy is released from the lungs. Extracting an epoch accurately is important in speech synthesis, analysis, and pitch oriented studies. The time-varying characteristics of the s
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Super-Resolution With Noisy Measurements: Reconciling Upper And Lower Bounds
This paper considers the problem of lower bounding the mean-squared-error (MSE) of unbiased super-resolution estimates. In literature, only upper bounds on the MSE are available which scale with the so-called super-resolution factor (SRF). However, the up
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A Model-Based Deep Network For Mri Reconstruction Using Approximate Message Passing Algorithm
We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN block
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Achieving The Capacity Of The Dna Storage Channel
Significant advances in biochemical technologies, such as synthesizing and sequencing devices, have made DNA a competitive medium for archival data storage. In this paper we analyze storage systems based on these macromolecules from an information theoret
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Filtering Out Time-Frequency Areas Using Gabor Multipliers
We address the problem of filtering out localized time-frequency components in signals. The problem is formulated as a minimization of a suitable quadratic form, that involves a data fidelity term on the short-time Fourier transform outside the support of
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Cramer-Rao Bound On Doa Estimation Of Finite Bandwidth Signals Using A Moving Sensor
In this paper, we provide a framework for the direction of arrival (DOA) estimation using a moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to s
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Exploiting Vocal Tract Coordination Using Dilated Cnns For Depression Detection In Naturalistic Environments
Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordinati
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Boosted Locality Sensitive Hashing: Discriminative Binary Codes For Source Separation
Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity. In this study, we propose an adaptive boosting approach to learning locality sensitive hash
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Shadow Removal Of Text Document Images By Estimating Local And Global Background Colors
This paper proposes a simple yet effective method for removing shadows from text document images. Assuming that the document mainly contains texts, our method estimates the global and local background colors using statistical analysis of the whole image a
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Gaussian Processes Over Graphs
Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given th
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A Comprehensive Framework For 2D-Jnd Extension To 360-Deg Images
Masking effect is one of the most important perceptual properties that could be modeled by estimating an adaptive threshold known as the just noticeable difference (JND) referring to the maximum difference not perceived by the human visual system (HVS). I
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Eigenbeam-Esprit For Doa-Vector Estimation
Several techniques exist to estimate the directions of arrival (DOAs) of sound sources captured with a spherical microphone array. The eigenbeam rotational invariance technique (EB-ESPRIT) uses recurrence relations of spherical harmonics to estimate the D
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Ertis: Real-Time 3D Acoustic Sonar Imaging Using Sparse Microphone Arrays
In recent years, our research group has developed state of the art 3D sonar sensors which use a low-cost MEMS microphone array for real-time acoustic imaging in air. Using this sensor, various robotic applications have been developed, including obstacle a
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A Novel Method For Obtaining Diffuse Field Measurements For Microphone Calibration
NOVELTY OF THE DEMO: Is it possible to obtain a diffused field response of a microphone array and perform calibration in under a minute? If such a method exists, is it possible to achieve an accuracy of half a dB from the expected response? The answer to
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From Compressed Sensing to Deep Learning: Tasks, Structures, and Models
From Compressed Sensing to Deep Learning: Tasks, Structures, and Models.
Presenter: Yonina Eldar, ICASSP 2020.
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Attentive Item2Vec: Neural Attentive User Representations
Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendations that are presented to the user. For example, in the case of movie recomm
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Supervised Canonical Correlation Analysis Of Data On Symmetric Positive Definite Manifolds By Riemannian Dimensionality Reduction
Most computer vision problems entail data that reside on Riemannian manifolds. Canonical correlation analysis (CCA) is a powerful method that captures correlations between any two sets of matrices. In this paper, we propose a framework for a supervised CC
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Dynamic Oversampling In 1-Bit Quantized Asynchronous Large-Scale Multiple-Antenna Systems For Sustainable Iot Networks
In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the
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Conditional Density Driven Grid Design In Point-Mass Filter
The paper is devoted to the state estimation of nonlinear stochastic dynamic systems. The stress is laid on a grid-based numerical solution to the Bayesian recursive relations using the point-mass filter (PMF). In the paper, a novel conditional density dr
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Camera Configuration Design In Cooperative Active Visual 3D Reconstruction: A Statistical Approach
Visual 3D reconstruction is an essential technique in computer vision which restores the 3D model of the scene from multi-view images. In this paper, we propose a statistical framework for the active visual 3D reconstruction. We first derive a closed-form
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A Real Time Implementation Of A Bayer Domain Image Deblurring Core For Optical Blur Compensation
In this letter, we present an implementation of deblurring hardware to mitigate blur incurred by optical aberrations in a real-time manner to increase resolution for mobile camera modules. As optical aberrations tend to be variant according to spatial loc
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Trace Norm Generative Adversarial Networks For Sensor Generation And Feature Extraction
Generative Adversarial Networks (GANs) have been shown effective to generate realistic enough sensor data for industrial failure prediction. Compared to computer vision problems, where it is very common to have more than 1000 classes, the number of classe
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A Multichannel Kalman-Based Wiener Filter Approach For Speaker Interference Reduction In Meetings
Recording a meeting and obtaining clean speech signals of each speaker is a challenging task. Even with a multichannel recording, in which all speakers are equipped with a close-talk microphone, speech of an active speaker still couples not only into his
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Simplified Dynamic Sc-Flip Polar Decoding
SC-Flip (SCF) decoding is a low-complexity polar code decoding algorithm alternative to SC-List (SCL) algorithm with small list sizes. To achieve the performance of the SCL algorithm with large list sizes, the Dynamic SC-Flip (DSCF) algorithm was proposed
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Full Reference Video Quality Measures Improvement Using Neural Networks
The accuracy of video quality metrics (VQMs) is an important issue for several applications. In this work, first we observe that the accuracy of several video quality metrics (VQMs) is strongly related to the spatial complexity index (SI) of the source. I
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Non-Uniform Video Time-Lapse Method Based On Motion Scenario And Stabilization Constraint
Time-lapse of user captured video becomes popular in many applications recently, non-uniform sampling and digital video stabilization (VS) are usually two independent steps to keep meaningful contents and provide stabilized output. However, non-uniform sa
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Federated Learning With Quantization Constraints
Traditional deep learning models are trained on centralized servers using labeled sample data collected from edge devices. This data often includes private information, which the users may not be willing to share. Federated learning (FL) is an emerging ap