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Densely Connected Neural Network With Dilated Convolutions For Real-Time Speech Enhancement In The Time Domain
In this work, we propose a fully convolutional neural network for real-time speech enhancement in the time domain. The proposed network is an encoder-decoder based architecture with skip connections. The layers in the encoder and the decoder are followed
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Person Identification Using Deep Convolutional Neural Networks On Short-Term Signals From Wearable Sensors
In this work, we explore the discriminating ability of short-term signal patterns (e.g. few minutes long) with respect to the person identification task. We focus on signals recorded by simple wearable devices, such as smartwatches, which can measure move
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Multimodal Learning For Classroom Activity Detection
Classroom activity detection (CAD) focuses on accurately classifying whether the teacher or student is speaking and recording both the length of individual utterances during a class. A CAD solution helps teachers get instant feedback on their pedagogical
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Attention Guided Region Division For Crowd Counting
Crowd counting has drawn more and more attention in computer vision. There are two mainstream approaches to deal with crowd counting tasks, regression and detection. Regression-based methods usually overestimate the count in sparse areas, while detection-
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Speaker Independence Of Neural Vocoders And Their Effect On Parametric Resynthesis Speech Enhancement
Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better quality speech enhancement. We term this parametric resynthesis (PR). In pre
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Unsupervised Content-Preserved Adaptation Network For Classification Of Pulmonary Textures From Different Ct Scanners
Deep network based methods have been proposed for accurate classification of pulmonary textures on CT images. However, such methods well-trained on CT data from one scanner cannot perform well when they are directly applied to the data from other scanners
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Improving The Scalability Of Deep Reinforcement Learning-Based Routing With Control On Partial Nodes
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Machine Learning (ML)-based routing optimization has been proposed to optimize the performance of flow routing for future networks, such as Software-Defined Networks (SDNs). However, existing studies are either hard to converge for large networks or vulne
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Q-Gadmm: Quantized Group Admm For Communication Efficient Decentralized Machine Learning
In this paper, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct
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Speaker Diarization Using Latent Space Clustering In Generative Adversarial Network
In this work, we propose deep latent space clustering for speaker diarization using generative adversarial network (GAN) back-projection with the help of an encoder network. The proposed diarization system is trained jointly with GAN loss, latent variable
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Adaptive Distributed Stochastic Gradient Descent For Minimizing Delay In The Presence Of Stragglers
We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on $n$ workers each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who
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Gpu-Accelerated Viterbi Exact Lattice Decoder For Batched Online And Offline Speech Recognition
We present an optimized weighted finite-state transducer (WFST) decoder capable of online streaming and offline batch processing of audio using Graphics Processing Units (GPUs). The decoder is efficient in memory utilization, input/output (I/O) bandwidth,
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Tree Of Shapes Cut For Material Segmentation Guided By A Design
In manufacturing, the monitoring of the fabrication process is crucial in order to be sure that objects are compliant. For nano-objects, most of this monitoring is done manually. In this paper, we propose a method to segment different materials in a manuf
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Variational Student: Learning Compact And Sparser Networks In Knowledge Distillation Framework
The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To this end, we propose Variational Student where we reap the benefits of com
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Rde-Moga: Automatic Selection Of Rate-Distortion-Energy Control Points For Video Encoders Using Muti-Objetive Genetic Algorithm
Controlling energy consumption of video encoders is acomplex multi-objective optimization problem of great im-portance. In this work we propose the RDE-MOGA, an multi-objective genetic algorithm capable of finding energeticallyefficient configurations for
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Emotional Voice Conversion Using Multitask Learning With Text-To-Speech
Voice conversion (VC) is a task that alters the voice of a person to suit different styles while conserving the linguistic content. Previous state-of-the-art technology used in VC was based on the sequence-to-sequence (seq2seq) model, which could lose lin
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A Novel Approach For Intelligibility Assessment In Dysarthric Subjects
Dysarthria is a motor speech impairment caused by muscle weakness. Individuals, with this condition, are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication.
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Sensor Selection For Model-Free Source Localization: Where Less Is More
The ability for a wireless network to precisely localize the radio nodes composing it is a great tool towards system optimization and is increasingly seen as a basic service requirement. In the past, model-free algorithms such as weighted centroid localiz
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Epigraphical Reformulation For Non-Proximable Mixed Norms
This paper proposes an epigraphical reformulation (ER) technique for non-proximable mixed norm regularization. Various regularization methods using "mixed norms" have been proposed, where their optimization relies on efficient computation of the proximity
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A Study Of Child Speech Extraction Using Joint Speech Enhancement And Separation In Realistic Conditions
In this paper, we design a novel joint framework of speech enhancement and speech separation for child speech extraction in realistic conditions, targeting the problem of extracting child speech from daily conversations in BabyTrain mega corpus. To the be
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Back-And-Forth Prediction For Deep Tensor Compression
Recent AI applications such as Collaborative Intelligence with neural networks involve transferring deep feature tensors between various computing devices. This necessitates tensor compression in order to optimize the usage of bandwidth-constrained channe
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Transfer Learning From Youtube Soundtracks To Tag Arctic Ecoacoustic Recordings
Sound provides a valuable tool for long-term monitoring of sensitive animal habitats at a spatial scale larger than camera traps or field observations, while also providing more details than satellite imagery. Currently, the ability to collect such record
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Preconditioned Ghost Imaging Via Sparsity Constraint
Ghost imaging via sparsity constraint (GISC) can recover objects from the intensity fluctuation of light fields at a sampling rate far below the Nyquist rate. However, its imaging quality may degrade severely when the coherence of sampling matrices is lar
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Learning-Based Content Caching And User Clustering: A Deep Deterministic Policy Gradient Approach
The joint design of content caching and user clustering (JCC) in cache-enabled heterogeneous networks is challenging, due to various unknown, possibly time-varying, system parameters which potentially give rise to various design tradeoffs in practice. Thi
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Upgrading Crfs To Jrfs And Its Benefits To Sequence Modeling And Labeling
Two important sequence tasks are sequence modeling and labeling. Sequence modeling involves determining the probabilities of sequences, e.g. language modeling. It is still difficult to improve language modeling with additional relevant tags, e.g. part-of-
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Accent Estimation Of Japanese Words From Their Surfaces And Romanizations For Building Large Vocabulary Accent Dictionaries
In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence. However, there are a limited number of publicly available accent dictionaries, and those dictionaries e.g. UniDic, do not contain many compound words, prope
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Communication Constrained Learning With Uncertain Models
We consider the problem of distributed inference of a group of agents in a social network, where the agents construct, share, and update beliefs in a non-Bayesian framework to identify the underlying true state of the world. We build upon the concept of u
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Leveraging Ordinal Regression With Soft Labels For 3D Head Pose Estimation From Point Sets
Head pose estimation from depth image is a challenging problem, considering its large pose variations, severer occlusions, and low quality of depth data. In contrast to existing approaches that take 2D depth image as input, we propose a novel deep regress
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Optimal Window Design For W-Ofdm
Windowing is an effective approach to reduce out-of-band radiation (OBR) in multicarrier systems in order to avoid adjacent channel interference. However, commonly used window functions are chosen in an ad hoc manner and fixed. We present an optimal windo
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Multi-Motifgan (Mmgan): Motif-Targeted Graph Generation And Prediction
Generative graph models create instances of graphs that mimic the properties of real-world networks. Generative models are successful at retaining pairwise associations in the underlying networks but often fail to capture higher-order connectivity pattern
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A Study Of Generalization Of Stochastic Mirror Descent Algorithms On Overparameterized Nonlinear Models
We study the convergence, the implicit regularization and the generalization of stochastic mirror descent (SMD) algorithms in overparameterized nonlinear models, where the number of model parameters exceeds the number of training data points. Due to overp
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Nonlinear Spatial Filtering For Multichannel Speech Enhancement In Inhomogeneous Noise Fields
A common processing pipeline for multichannel speech enhancement is to combine a linear spatial filter with a single-channel postfilter. In fact, it can be shown that such a combination is optimal in the minimum mean square error (MMSE) sense if the noise
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A New Application Of Ultrasound Signal Processing For Archaeological Ceramic Classification
Identifying archaeological ceramic pieces is a challenging problem for archaeologists, since fragments of archaeological pottery from the same site might have been made in different distant locations from the site. The pieces look very similar and context
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One-Bit Doa Estimation Via Sparse Linear Arrays
Parameter estimation from noisy and quantized received signals has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. Techniques to achieve high estimation performance in spite of the coarse qua
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Fast Optical System Identification By Numerical Interferometry
We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse problem
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A Deep Learning Architecture For Epileptic Seizure Classification Based On Object And Action Recognition
Epilepsy affects approximately 1% of the world’s population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to c
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Hybrid Neural-Parametric F0 Model For Singing Synthesis
We propose a novel hybrid neural-parametric fundamental frequency generation model for singing voice synthesis. A recurrent neural network predicts the parameters of a flexible parametric F0 model, conditioned on a given input score. Rather than trying to
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Automatic Classification Of Volumes Of Water Using Swallow Sounds From Cervical Auscultation
The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can chara
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Anomalous Sound Detection Based On Interpolation Deep Neural Network
As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoenc
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Localized Linear Regression In Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (gene
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Connections Between Spectral Properties Of Asymptotic Mappings And Solutions To Wireless Network Problems
We establish connections between asymptotic functions and properties of solutions to problems in wireless networks. We start by introducing self-mappings (called asymptotic mappings) constructed with asymptotic functions, and we show that their spectral p
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Super-Resolution Via Image-Adapted Denoising Cnns: Incorporating External And Internal Learning
While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mis
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Learning Based Reconfigurable Wideband Non-Contiguous Spectrum Characterization For 5G Applications
Introduction of spectrum sharing in 3GPP Release 17 demands base-stations (BS) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Since, multiple-antenna and beam-forming
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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