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A Regularization Framework For Learning Over Multitask Graphs
This letter proposes a general regularization framework for inference over multitask networks. The optimization approach relies on minimizing a global cost consisting of the aggregate sum of individual costs regularized by a term that allows to incorporat
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Optimal Leak Factor Selection For The Output-Constrained Leaky Filtered-Input Least Mean Square Algorithm
The leaky filtered-input least mean square (LFxLMS) algorithm is widely used in active noise control applications to minimize the degradation of attenuation performance due to output saturation distortion. However, the leak factor, which is critical in de
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Pre-Training In Deep Reinforcement Learning For Automatic Speech Recognition
Deep reinforcement learning (deep RL) is a combination of deep learning and reinforcement learning principles. it creates efficient methods that can learn by interacting with its environment. Deep RL led to breakthroughs in many complex tasks that were pr
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Multilingual Acoustic Word Embedding Models For Processing Zero-Resource Languages
Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems.
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Impact Of A Shift-Invariant Harmonic Phase Model In Fully Parametric Harmonic Voice Representation And Time/Frequency Synthesis
Harmonic representation models are widely used, notably in speech coding and synthesis. In this paper, we describe two fully parametric harmonic representation and signal reconstruction alternatives that rely on a shift-invariant harmonic phase model and
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Linear Model-Based Intra Prediction In Vvc Test Model
This paper studies a new intra prediction method based on a linear model for improving the intra prediction performance of Versatile Video Coding (H.266/VVC) standard. The Linear Model-based Intra Prediction (LMIP) method in this work attempts to model th
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Augmentation Data Synthesis Via Gans: Boosting Latent Fingerprint Reconstruction
Latent fingerprint reconstruction is a vital preprocessing step for its identification. This task is very challenging due to not only existing complicated degradation patterns but also its scarcity of paired training data. To address these challenges, we
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Misspecified Cramer-Rao Bound For Delay Estimation With A Mismatched Waveform: A Case Study
In this paper we investigate the problem of time of arrival estimation which occurs in many real-world applications, such as indoor localization or non-destructive testing via ultrasound or radar. A problem that is often overlooked when analyzing these sy
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In-Domain And Out-Of-Domain Data Augmentation To Improve Children's Speaker Verification System In Limited Data Scenario
In this paper, we present our efforts towards developing a robust automatic speaker verification (ASV) system for children when the domain-specific data is limited. For that purpose, we have studied the effect of in-domain and out-of-domain data augmentat
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Multi-Task Learning In Autonomous Driving Scenarios Via Adaptive Feature Refinement Networks
Many deep learning applications benefit from multi-task learning with several related objectives. In autonomous driving scenarios, being able to accurately infer motion and spatial information is essential for scene understanding. In this paper, we combin
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Anti-Jamming Routing For Internet Of Satellites: A Reinforcement Learning Approach
The anti-jamming routing for the Internet of Satellites (IoS) has drawn increasing attentions due to the unknown interrupts, unexpected congestion and smart jamming. This paper investigates anti-jamming routing scheme for heterogeneous IoS, with the aim o
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Frequency And Temporal Convolutional Attention For Text-Independent Speaker Recognition
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end. In this paper, we propose methods of co
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Admm-Based One-Bit Quantized Signal Detection For Massive Mimo Systems With Hardware Impairments
This paper considers signal detection in massive multiple-input multiple-output (MIMO) systems with general additive hardware impairments and one-bit quantization. First, we present the quantization-unaware and Bussgang decomposition-based linear receiver
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Back-To-Back Butterfly Network, An Adaptive Permutation Network For New Communication Standards
In this paper, we introduce an adaptive Back-to-Back Butterfly Network (B?BN) dedicated to next communication standards. It can perform any kind of permutation, and its architecture is based on a concatenation of basic networks. However for a set of permu
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Low-Rank Tensor Ring Model For Completing Missing Visual Data
Low rank tensor factorization can be viewed as a higher order generalization of low-rank matrix factorization, both of which have been used for image and video representation and reconstruction from compressive measurements. In this paper, we present an a
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Robust Unsupervised Audio-Visual Speech Enhancement Using A Mixture Of Variational Autoencoders
Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a non-negative matrix factorization (NMF) model for noise variance to perform unsupervised speech enhancement. When visual d
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Coded Illumination And Multiplexing For Lensless Imaging
Mask-based lensless cameras offer an alternative option to conventional cameras. Compared to conventional cameras, lensless cameras can be extremely thin, flexible, and light-weight. Despite these advantages, the quality of images recovered from the lensl
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Detecting Multiple Speech Disfluencies Using A Deep Residual Network With Bidirectional Long Short-Term Memory
Stuttering is a speech impediment affecting tens of millions of people on an everyday basis. Even with its commonality, there is minimal data and research on the identification and classification of stuttered speech. This paper tackles the problem of dete
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Attention Mechanism Enhanced Kernel Prediction Networks For Denoising Of Burst Images
Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adop
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This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)--ba
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A Dsp Acceleration Framework For Software-Defined Radios On X86_64
This paper presents a DSP acceleration and assessment framework targeting SDR platforms on x86_64 architectures. Driven by the potential of rapid prototyping and evaluation of breakthrough concepts that these platforms provide, our work builds upon the we
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Constant Envelope Massive Mimo-Ofdm Precoding: An Improved Formulation And Solution
Constant Envelope (CE) precoding is an efficient technique for systems based on massive antenna arrays since the constant amplitude of the transmit signal facilitates the use of power efficient non-linear transmitter circuitry, such as power amplifiers (P
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A Cross-Task Transfer Learning Approach To Adapting Deep Speech Enhancement Models To Unseen Background Noise Using Paired Senone Classifiers
We propose an environment adaptation approach that improves deep speech enhancement models via minimizing the Kullback- Leibler divergence between posterior probabilities produced by a multi-condition senone classifier (teacher) fed with noisy speech feat
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Optimum Kernel Particle Filter For Asymmetric Laplace Noise
In this paper we present on-line Bayesian filtering methods for time series models corrupted by asymmetric Laplace noise. An optimum kernel particle filter is designed for the general asymmetric case, and its performance is compared to that of a tradition
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Aipnet: Generative Adversarial Pre-Training Of Accent-Invariant Networks For End-To-End Speech Recognition
As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accent
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Mobility-Aware Beam Steering In Metasurface-Based Programmable Wireless Environments
Programmable wireless environments (PWEs) utilize electromagnetic metasurfaces to transform wireless propagation into a software-controlled resource. In this work we study the effects of user device mobility on the efficiency of PWEs. An analytical model
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Knowledge Enhanced Latent Relevance Mining For Question Answering
Answer selection which aims to select the most appropriate answer from a pre-selected candidate pool has become increasingly important in a variety of practical applications. Previous work tends to use complex attention mechanisms to capture contextual re
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On Network Science And Mutual Information For Explaining Deep Neural Networks
In this paper, we present a new approach to interpreting deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information al
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A Memory Augmented Architecture For Continuous Speaker Identification In Meetings
We introduce and analyze a novel approach to the problem of speaker identification in multi-party recorded meetings. Given a speech segment and a set of available candidate profiles, a data-driven approach is proposed learning the distance relations betwe
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New Metrics For Evaluating The Accuracy Of Fundamental Frequency Estimation Approaches In Musical Signals
This paper demonstrates the importance of assessing the performance of fundamental frequency estimation algorithms on note-level descriptors in addition to frame-level accuracy. Note-level descriptors provide a better description of the human experience o
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Adversarial Mixup Synthesis Training For Unsupervised Domain Adaptation
Domain adversarial training is a popular approach for Unsupervised Domain Adaptation~(DA). However, the transferability of adversarial training framework may drop greatly on the adaptation tasks with a large distribution divergence between source and targ
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Compare Learning: Bi-Attention Network For Few-Shot Learning
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods called metric learning addresses this challenge by first learning a deep dista
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Robust Frequency-Domain Recursive Least M-Estimate Adaptive Filter For Acoustic System Identification
To identify acoustic systems in non-Gaussian and Gaussian noises, a robust frequency-domain recursive least M-estimate (FRLM) adaptive filtering algorithm is proposed. The cost function of the adaptive filter is defined by using a robust time-domain M-est
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Vggsound: A Large-Scale Audio-Visual Dataset
Our goal is to collect a large-scale audio-visual dataset with low label noise from videos `in the wild' using computer vision techniques. The resulting dataset can be used for training and evaluating audio recognition models. We make three contributions.
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A Simple And Efficient Iterative Method For Toa Localization
This paper develops a simple and efficient method for source localization using signal time-of-arrival (TOA) measurements. There exist many TOA localization algorithms, most of which require matrix inversions. Their complexity often makes them unsuitable
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Automatic And Simultaneous Adjustment Of Learning Rate And Momentum For Stochastic Gradient-Based Optimization Methods
Stochastic gradient-based methods are prominent for training machine learning and deep learning models. The performance of these techniques depends on their hyperparameter tuning over time and varies for different models and problems. Manual adjustment of
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Guided Learning For Weakly-Labeled Semi-Supervised Sound Event Detection
We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single mo
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Transmit Beampattern Shaping Via Waveform Design In Cognitive Mimo Radar
This paper is focused on designing a set of constant modulus waveform for cognitive Multiple-Input Multiple-Output (MIMO) radar systems. The aim is to shape the beampattern in transmitter to minimize the Integrated Side-lobe Level (ISL) in spatial domain
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Power Optimization Using Embedded Automatic Gain Control Algorithm With Photoplethysmography Signal Quality Classification
This paper presents the design and implementation of an Automatic Gain Control (AGC) embedded algorithm for photoplethysmographic (PPG) sensors. We use a number of statistical and spectral characteristics of the raw and filtered PPG signals, referred to a
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Fir Filter Design And Implementation For Phase-Based Processing
Complex steerable pyramid (CSP) is widely used to decompose images into muti-scale and oriented subbands for phase-based processing, such as video magnification, frame interpolation, and view synthesis. The conventional implementation is based on frequenc
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Low-Tubal-Rank Tensor Recovery From One-Bit Measurements
This paper focuses on the recovery of low-tubal-rank tensors from binary measurements under the frame of tensor Singular Value Decomposition. We show that the direction of a tubal-rank-$r$ tensor $m{mathcal{X}}in R^{n_1 imes n_2 imes n_3}$ can be a
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We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and accompaniment. This approach
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Cooperative Learning Via Federated Distillation Over Fading Channels
Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated Learning (FL). An alternative solution with reduced communication ove
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Complex Transformer: A Framework For Modeling Complex-Valued Sequence
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers. However, speech, signal and audio data are naturally complex-valued after Fourier Transform, and studies ha
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Robust Matrix Completion Via Lp-Greedy Pursuits
A novel $ell_p$-greedy pursuit (GP) algorithm for robust matrix completion, i.e., recovering a low-rank matrix from only a subset of its noisy and outlier-contaminated entries, is devised. The $ell_p$-GP uses the strategy of sequential rank-one update.
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Robust Tdoa Indoor Tracking Using Constrained Measurement Filtering And Grid-Based Filtering
This paper considers exploiting the time difference of arrival (TDOA) measurements from a ultra wideband (UWB) indoor positioning system to locate a moving point target. In indoor environments, measured TDOAs are subject to large errors due to multipath a
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Sequence-Level Consistency Training For Semi-Supervised End-To-End Automatic Speech Recognition
This paper presents a novel semi-supervised end-to-end automatic speech recognition (ASR) method that employs consistency training with the use of unlabeled data. In consistency training, unlabeled data can be utilized for constraining a model such that i