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Hierarchical Sequence Representation With Graph Network
Video classification problem is a challenging task in computer vision. The performance of this task is highly relied on the scale of training data and the effectiveness of video embedding via a robust embedding network. Unsupervised solutions such as feat
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High-Resolution Attention Network With Acoustic Segment Model For Acoustic Scene Classification
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The spectral information of acoustic scenes is diverse and complex, which poses challenges for acoustic scene tasks. To improve the classification performance, a variety of convolutional neural networks (CNNs) are proposed to extract richer semantic infor
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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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An Empirical Study On Acoustic Feedback Path Across Hearing Aid Users
Acoustic feedback is one of the major problems in hearing aid applications. During a fitting session of a modern hearing aid, typically a feedback path prediction or an in situ measurement of feedback path is used as part of the gain and earpiece prescrip
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Fast Block-Sparse Estimation For Vector Networks
While there is now a significant literature on sparse inverse covariance estimation, all that literature, with only a couple of exceptions, has dealt only with univariate (or scalar) networks where each node carries a univariate signal. However in many, p
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On Modeling Asr Word Confidence
We present a new method for computing ASR word confidences that effectively mitigates the effect of ASR errors for diverse downstream applications, improves the word error rate of the 1-best result, and allows better comparison of scores across different
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Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-based acoustic simulation method is capable of modeling occlusion, specular a
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Single-Shot Real-Time Multiple-Path Time-Of-Flight Depth Imaging For Multi-Aperture And Macro-Pixel Sensors
Multiple-Path Interference (MPI) is a major drawback of Time-of-Flight (ToF) sensors. MPI occurs when a ToF pixel receives more than a single light bounce from the scene. Current methods resolving more than a single return per pixel rely on the sequential
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Design-Gan: Cross-Category Fashion Translation Driven By Landmark Attention
The rise of generative adversarial networks has boosted a vast interest in the field of fashion image-to-image translation. However, previous methods do not perform well in cross-category translation tasks, e.g., translating jeans to skirts in fashion ima
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Near-Optimal Interference Exploitation 1-Bit Massive Mimo Precoding Via Partial Branch-And-Bound
In this paper, we focus on 1-bit precoding for large-scale antenna systems in the downlink based on the concept of constructive interference (CI). By formulating the optimization problem that aims to maximize the CI effect subject to the 1-bit constraint
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Theoretical Analysis Of Multi-Carrier Agile Phased Array Radar
Modern radar systems are expected to operate reliably in congested environments under cost and power constraints. A recent technology for realizing such systems is frequency agile radar (FAR), which transmits narrowband pulses in a frequency hopping manne
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A Bin Encoding Training Of A Spiking Neural Network Based Voice Activity Detection
Advances of deep learning for Artificial Neural Networks(ANNs) have led to significant improvements in the performance of digital signal processing systems implemented on digital chips. Although recent progress in low-power chips is remarkable, neuromorph
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Adaptive Knowledge Distillation Based On Entropy
Knowledge distillation (KD) approach is widely used in the deep learning field mainly for model size reduction. KD utilizes soft labels of teacher model, which contain the dark- knowledge that one-hot ground-truth does not have. This knowledge can improve
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Audio-Based Auto-Tagging With Contextual Tags For Music
Music listening context such as location or activity has been shown to greatly influence the users' musical tastes. In this work, we study the relationship between user context and audio content in order to enable context-aware music recommendation agnost
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Discrete Wasserstein Autoencoders For Document Retrieval
Learning to hash via generative models has become a promising paradigm for fast similarity search in document retrieval. The binary hash codes are treated as Bernoulli latent variables when training a variational autoencoder (VAE). However, the prior of d
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On Harmonic Approximations Of Inharmonic Signals
In this work, we present the misspecified Gaussian Cram'er-Rao lower bound for the parameters of a harmonic signal, or pitch, when signal measurements are collected from an almost, but not quite, harmonic model. For the asymptotic case of large sample si
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Denoising Of Event-Based Sensors With Spatial-Temporal Correlation
As a novel asynchronous-driven cameras, event-based sensors are with high sensitivity, fast speed and low data volume, but with abundant noise. Since the output of event-based sensors is in the form of address-event-representation (AER), the traditional f
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Dense Residual Network For Retinal Vessel Segmentation
Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such as hypertension and diabetes. In recent research, numerous successful segme
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Video Frame Interpolation Via Residue Refinement
Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames. Although great success has been achieved by deep neural networks, the synthesized images stills suffer from poor visual appearance and unsatisfie
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Single-Channel Speech Separation Integrating Pitch Information Based On A Multi Task Learning Framework
Pitch is a critical cue for speech separation in humans? auditory perception. Although the technology of tracking pitch in single-talker speech succeeds in many applications, it?s still a challenging problem to extract pitch information from speech mixtur
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Low Mutual And Average Coherence Dictionary Learning Using Convex Approximation
In dictionary learning, a desirable property for the dictionary is to be of low mutual and average coherences. Mutual coherence is defined as the maximum absolute correlation between distinct atoms of the dictionary, whereas the average coherence is a mea
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Robust Online Mirror Saddle-Point Method For Constrained Resource Allocation
Online-learning literature has focused on designing algorithms that ensure sub-linear growth of the cumulative long-term constraint violations. The drawback of this guarantee is that strictly feasible actions may cancel out constraint violations on other
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Adaptive Subspace Detectors For Off-Grid Mismatched Targets
Abstract In classical detection framework, the parameter space is usually discretized, so that in reality received parameter dependent signals are never perfectly aligned with the signal model under test: it leads to the off-grid signal mismatch. In a Gau
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Corrgan: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks
We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts
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Triggerless Random Interleaved Sampling
A single short sequence of samples taken at sub-Nyquist rate rarely allows for periodic signal recovery. If there is more than one such sequence and time offsets between these sequences are given, the signal approximation is possible and is known as equiv
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Ava Active Speaker: An Audio-Visual Dataset For Active Speaker Detection
Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio
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Self-Supervised Deep Learning For Fisheye Image Rectification
To rectify fisheye distortion from a single image, we advance self-supervised learning strategies and propose a unique deep learning model of Fisheye GAN (FE-GAN). Our FEGAN learns pixel-level distortion flow from sets of fisheye distorted images and dist
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Efficient Techniques For In-Band System Information Broadcast In Multi-Cell Massive Mimo
In this paper we consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We propose two different m
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Speech Emotion Recognition With Dual-Sequence Lstm Architecture
Speech Emotion Recognition (SER) has emerged as a critical component of the next generation of human-machine interfacing technologies. In this work, we propose a new dual-level model that predicts emotions based on both MFCC features and mel-spectrograms
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Online Community Detection By Spectral Cusum
We present an online community change detection algorithm called {it spectral CUSUM} to detect the emergence of a community using a subspace projection procedure based on a Gaussian model setting. Theoretical analysis is provided to characterize the aver
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Channel-Attention Dense U-Net For Multichannel Speech Enhancement
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce c
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Neural Lattice Search For Speech Recognition
To improve the accuracy of automatic speech recognition, a two-pass decoding strategy is widely adopted. The first-pass model generates compact word lattices, which are utilized by the second-pass model to perform rescoring. Currently, the most popular re
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Beam-Tasnet: Time-Domain Audio Separation Network Meets Frequency-Domain Beamformer
Recent studies have shown that acoustic beamforming using a microphone array plays an important role in the construction of high-performance automatic speech recognition (ASR) systems, especially for noisy and overlapping speech conditions. In parallel wi
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We propose a perceptual encryption within JPEG compression (EWJ). Although some of the conventional EWJs have the `tunability,' which is a property of how many perceptual degradation levels can be provided with the single encryption technique, it is insuf
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Processing Convolutional Neural Networks On Cache
With the advent of Big Data application domains, several Machine Learning (ML) signal-processing algorithms such as Convolutional Neural Networks (CNNs) are required to process progressively larger datasets at a great cost in terms of both compute power a
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Vimo: Vital Sign Monitoring Using Commodity Millimeter Wave Radio
Accurate monitoring of human vital signs (e.g. breathing and heart rates) is crucial in detecting medical problems. In this paper, we propose ViMo, a calibration-free remote Vital sign Monitoring system that can simultaneously monitor multiple users by le
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A Recursive Bayesian Solution For The Excess Over Threshold Distribution With Stochastic Parameters
In this paper, we propose a new approach for analyzing extreme values that are witnessed in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time and, as opposed to modeling just the maximum of
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Computing Hilbert Transform And Spectral Factorization For Signal Spaces Of Smooth Functions
Although the Hilbert transform and the spectral factorization are of central importance in signal processing, both operations can generally not be calculated in closed form. Therefore, algorithmic solutions are prevalent which provide an approximation of
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Content Based Singing Voice Extraction From A Musical Mixture
We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content. Our model follows an encoder decoder architecture and takes as input the magnitude component of the spe
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Line Spectral Estimation With Palindromic Kernels
Estimation of line spectra is a classical problem in signal processing and arises in many applications. The problem is to estimate the frequencies and corresponding amplitudes of a sum of (possibly complex-valued) sinusoidal components from noisy measurem
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Confidence Estimation For Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks
Recently, there has been growth in providers of speech transcription services enabling others to leverage technology they would not normally be able to use. As a result, speech-enabled solutions have become commonplace. Their success critically relies on
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Clutter Identification Based On Sparse Recovery And L1-Type Probabilistic Distance Measures
Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there a
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Spoken Document Retrieval Leveraging Bert-Based Modeling And Query Reformulation
Spoken document retrieval (SDR) has long been deemed a fundamental and important step towards efficient organization of, and access to multimedia associated with spoken content. In this paper, we present a novel study of SDR leveraging the Bidirectional E
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Efficient Image Super Resolution Via Channel Discriminative Deep Neural Network Pruning
Deep convolutional neural networks (CNN) have demonstrated superior performance in image super-resolution (SR) problem.However, CNNs are known to be heavily over-parameterized, and suffer from abundant redundancy. The growing size ofCNNs may be incompatib