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Moga: Searching Beyond Mobilenetv3
The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods main
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A Geometric Approach For Unsupervised Similarity Learning
Metric learning groups similar examples together, while moving away dissimilar ones. This is a crucial task in image processing and computer vision. However, existing metric learning approaches require huge number of labeled examples for their success. In
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Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., aut
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Genetic Algorithm Optimized Support Vector Machine In Noma-Based Satellite Networks With Imperfect Csi
With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade th
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Statistical Signal Processing Approach For Rain Estimation Based On Measurements From Network Management Systems
In this paper we apply statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advant
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Low Complexity Nlms For Multiple Loudspeaker Acoustic Echo Canceller Using Relative Loudspeaker Transfer Functions
Speech signals captured by a microphone mounted to a smart soundbar or speaker are inherently contaminated by echos. Modern smart devices are usually characterized by low computational capabilities and low memory resources; in these cases, a low-complexit
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Fcem: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of Voip Stream Via Multi-Head Attention
Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to the
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Secure Symbol-Level Miso Precoding
While constructive interference offers indirect advantages in physical layer security by reducing the transmit power required to achieve a desired performance level, additional gains are possible by choosing the symbols to degrade the eavesdropper's abili
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On Binary Sequence Set Design With Applications To Automotive Radar
We consider herein the case of two vehicles equipped with multi-input multi-output (MIMO) automotive radars driving next to each other. We assume that 5G communications allow us to coordinate the radar probing waveforms for the vehicles. Then the binary s
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A Fast And Accurate Super-Resolution Network Using Progressive Residual Learning
Single-image super-resolution (SISR) task has witnessed great strides in the past few years with the development of deep learning. However, most existing studies concentrate on exploiting much deeper super-resolution networks, which are not friendly to th
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Pyannote.Audio: Neural Building Blocks For Speaker Diarization
We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to buil
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Overdetermined Independent Vector Analysis
We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources K is less than that of microphones M, and (ii) there are up to M - K stationary Gaussian noises that need not to
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Dnn-Chip Predictor: An Analytical Performance Predictor For Dnn Accelerators With Various Dataflows And Hardware Architectures
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it often takes months/years and requires cross-disciplinary knowledge. To enab
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Objective Bayesian Detection Under Spatially Correlated Gaussian Observations For Multi-Antenna Cognitive Radio Network
This paper develops an objective Bayesian detector for asserting the presence of primary user (PU) signal buried in additive noise/interference using a sequence of complex vector samples from a multi-antenna spectrum sensing system. The PU signal is zero
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Towards A New Understanding Of The Training Of Neural Networks With Mislabeled Training Data
We investigate the problem of machine learning with mislabeled training data. We try to make the effects of mislabeled training better understood through analysis of the basic model and equations that characterize the problem. This includes results about
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Action-Manipulation Attacks On Stochastic Bandits
As stochastic multi-armed bandit model has many important applications, understanding the impact of adversarial attacks on this model is essential for the safe applications of this model. In this paper, we propose a new class of attack named action-manipu
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Improving Speaker-Attribute Estimation By Voting Based On Speaker Cluster Information
This paper proposes a general post-processing method for improving speaker-attribute estimation. Estimating speaker-specific attributes such as age and gender is an important task with a wide range of applications. While the recent proposed deep neural ne
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Generalized Kernel-Based Dynamic Mode Decomposition
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that
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Optimized Single Carrier Transceiver For Future Sub-Terahertz Applications
The performance of sub-THz communications, contemplated for the next generation of wireless networks, are significantly degraded by oscillator phase noise. In this paper, we address the design of a single carrier transceiver resilient to phase noise. This
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Asymptotic Stochastic Analysis Of Partially Relaxed Dml
The Partial Relaxation (PR) approach has recently been proposed to solve the Direction of Arrival (DoA) estimation problem. In this paper, we investigate the outlier production mechanism of the Partially Relaxed Deterministic Maximum Likelihood (PR-DML) D
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Overcoming High Nanopore Basecaller Error Rates For Dna Storage Via Basecaller-Decoder Integration And Convolutional Codes
As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durabilit
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Multilingual Grapheme-To-Phoneme Conversion With Byte Representation
Grapheme-to-phoneme (G2P) models convert a written word into its corresponding pronunciation and are essential components in automatic-speech-recognition and text-to-speech systems. Recently, the use of neural encoder-decoder architectures has substantial
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Multi-Way Multi-View Deep Autoencoder For Image Feature Learning With Multi-Level Graph Regularization
Multi-view feature learning has garnered much attention recently since many real world data are comprised of different representations or views. How to explore the consensus structure and eliminate the inconsistency noise in different views remains a chal
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Spatial Active Noise Control Based On Kernel Interpolation With Directional Weighting
A spatial active noise control (ANC) method taking prior information on the approximate direction of primary noise sources into consideration is proposed. ANC aims to cancel incoming primary noise using secondary loudspeakers. Conventional multipoint ANC
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Using Automatic Speech Recognition And Speech Synthesis To Improve The Intelligibility Of Cochlear Implant Users In Reverberant Listening Environments
Cochlear implant (CI) users experience substantial difficulties in understanding reverberant speech. A previous study proposed a strategy that leverages automatic speech recognition (ASR) to recognize reverberant speech and speech synthesis to translate t
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Speaker-Invariant Affective Representation Learning Via Adversarial Training
Representation learning for speech emotion recognition is challenging due to labeled data sparsity issue and lack of gold-standard references. In addition, there is much variability from input speech signals, human subjective perception of the signals and
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Deep Neural Network Based Matrix Completion For Internet Of Things Network Localization
In this paper, we propose a deep neural network based matrix completion approach for Internet of Things (IoT) localization. In the proposed method, we recast Euclidean distance matrix completion problem into the alternating minimization problem. By using
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Peer To Peer Offloading With Delayed Feedback: An Adversary Bandit Approach
Fog computing brings computation and services to the edge of networks enabling real time applications. In order to provide satisfactory quality of experience, the latency of fog networks needs to be minimized. In this paper, we consider a peer computation
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Structured Sparse Attention For End-To-End Automatic Speech Recognition
The Softmax normalization function-based attention mechanism is often employed by End-to-End Automatic Speech Recognition (E2E ASR) models to tell the network where to focus within the input. However, this mechanism leads to the attention distribution bec
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Deep-Neural-Network Based Fall-Back Mechanism In Interference-Aware Receiver Design
In this paper, we consider designing a fall-back mechanism in an interference-aware receiver. Typically, there are two types of detectors dealing with interference, known as enhanced interference rejection combining (eIRC) and symbol-level interference ca
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On Divergence Approximations For Unsupervised Training Of Deep Denoisers Based On Stein’S Unbiased Risk Estimator
Recently, there have been several works on unsupervised learning for training deep learning based denoisers without clean images. Approaches based on Stein's unbiased risk estimator (SURE) have shown promising results for training Gaussian deep denoisers.
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A New Perspective For Flexible Feature Gathering In Scene Text Recognition Via Character Anchor Pooling
Irregular scene text recognition has attracted much attention from the research community, mainly due to the complexity of shapes of text in natural scene. However, recent methods either rely on shape-sensitive modules such as bounding box regression, or
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Scalable Kernel Learning Via The Discriminant Information
Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques. This work studies a supervised kernel learning methodology to optimize such mappings. We ut
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Optimal Laplacian Regularization For Sparse Spectral Community Detection
Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we formal
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Quantized Tensor Robust Principal Component Analysis
High-dimensional data structures, known as tensors, are fundamental in many applications, including multispectral imaging and color video processing. Compression of such huge amount of multidimensional data collected over time is of paramount importance,
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Separable Optimization For Joint Blind Deconvolution And Demixing
Blind deconvolution and demixing is the problem of reconstructing convolved signals and kernels from the sum of their convolutions. This problem arises in many applications, such as blind MIMO. In this work, we present a separable approach to blind deconv
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Unsupervised Pre-Training Of Bidirectional Speech Encoders Via Masked Reconstruction
We propose an approach for pre-training speech representations via a masked reconstruction loss. Our pre-trained encoder networks are bidirectional and can therefore be used directly in typical bidirectional speech recognition models. The pre-trained netw
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Combining Cgan And Mil For Hotspot Segmentation In Bone Scintigraphy
Bone scintigraphy is widely used to diagnose bone tumor and metastasis. Accurate hotspot segmentation from bone scintigraphy is of great importance for tumor metastasis diagnosis. In this paper, we propose a new framework to detect and extract hotspots in
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Rev-Ae: A Learned Frame Set For Image Reconstruction
Reversible residual network naturally extends the linear lifting scheme with no theoretic guarantee. In this paper, we propose a reversible autoencoder (Rev-AE) with this extended non-linear lifting scheme to improve image reconstruction. Nonlinear predic
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Multitask Learning For Darpa Lorelei’S Situation Frame Extraction Task
This paper describes a novel approach of multitask learning for an end-to-end optimization technique for document classification. The application motivation comes from the need to extract "Situation Frames (SF)" from a document within the context of DARPA
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Disentangled Multidimensional Metric Learning For Music Similarity
Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing. For this task, it is typically necessary to define a similarity metric to
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Improving End-To-End Speech Synthesis With Local Recurrent Neural Network Enhanced Transformer
Although Transformer based neural end-to-end TTS model has demonstrated extreme effectiveness in capturing long-term dependencies and achieved state-of-the-art performance, it still suffers from two problems. 1) limited ability to model sequential and loc
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Finite Sample Deviation And Variance Bounds For First Order Autoregressive Processes
In this paper, we study finite-sample properties of the least squares estimator in first order autoregressive processes. By leveraging a result from decoupling theory, we derive upper bounds on the probability that the estimate deviates by at least a posi
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Controllable Time-Delay Transformer For Real-Time Punctuation Prediction And Disfluency Detection
With the increased applications of automatic speech recognition (ASR) in recent years, it is essential to automatically insert punctuation marks and remove disfluencies in transcripts, to improve the readability of the transcripts as well as the performan
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Drss-Based Localisation Using Weighted Instrumental Variables And Selective Power Measurement
Differential received signal strength (DRSS) provides a practical means of localisation for wireless sensor networks. Closed-form location estimators based on a linearised propagation path loss model are computationally efficient and hence suitable for wi
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Multi-Task Learning For Voice Trigger Detection
We describe the design of a voice trigger detection system for smart speakers. We address two major challenges. The first is that the detectors are deployed in complex acoustic environments with external noise and loud playback by the device itself. Secon
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Learning Graph Influence From Social Interactions
In social learning, agents form their opinions or beliefs about certain hypotheses by exchanging local information. This work considers the recent paradigm of weak graphs, where the network is partitioned into sending and receiving components, with the fo
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Robustness Assessment Of Automatic Reinke’S Edema Diagnosis Systems
In the past few years there has been a great interest in computer aided diagnosis research. In the field of voice quality assessment, signal processing gives us tools to analyze and extract numeric characteristics describing the analyzed signal. These fea
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Deep Product Quantization Module For Efficient Image Retrieval
Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft qu