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A Return To Dereverberation In The Frequency Domain Using A Joint Learning Approach
Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigat
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Adaptation Of Rnn Transducer With Text-To-Speech Technology For Keyword Spotting
With the advent of recurrent neural network transducer (RNN-T) model, the performance of keyword spotting (KWS) systems has greatly improved. However, the KWS systems, employed for wake-word detection, still rely on the availability of keyword specific tr
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Regression Before Classification For Temporal Action Detection
Action classification combined with location regression is a widely-utilized mechanism in existing temporal action detection methods. However, there exists an inconsistency problem between locations and categories of action instances in this mechanism. Mo
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Resource Management In The Multibeam Noma-Based Satellite Downlink
A beam-free approach to channel allocation in a multi-beam four-color satellite coverage area is taken. Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) are compared as methods to serve users non-necessarily located on the refere
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Iq-Stan: Image Quality Guided Spatio-Temporal Attention Network For License Plate Recognition
License plate recognition (LPR) is one of the essential components in intelligent transportation systems. Although the image processing algorithms for LPR have been extensively studied in the past several years, the recognition performance is still not sa
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A Unified Sequence-To-Sequence Front-End Model For Mandarin Text-To-Speech Synthesis
In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components
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Unsupervised Key Hand Shape Discovery Of Sign Language Videos With Correspondence Sparse Autoencoders
Recognition of sign language is a difficult task which often requires tedious annotations by sign language experts. End-to-end learning attempts that bypass frame level annotations have achieved some success in limited datasets, but it has been shown that
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Self-Supervised Learning For Audio-Visual Speaker Diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video sy
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Balanced Binary Neural Networks With Gated Residual
Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt
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Robust Speaker Recognition Using Unsupervised Adversarial Invariance
In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architec
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Text Adaptation For Speaker Verification With Speaker-Text Factorized Embeddings
Text mismatch between pre-collected data, either training data or enrollment data, and the actual test data can significantly hurt text-dependent speaker verification (SV) system performance. Although this problem can be solved by carefully collecting dat
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Multi-View Clustering Via Mixed Embedding Approximation
This paper tackles multi-view clustering via proposing a novel mixed embedding approximation (MEA) method. Formally, we aim to learn a uniform orthogonal embedding based on the orthogonal pre-embeddings of each view. At first, we hope that the uniform emb
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Multilinear Generalized Singular Value Decomposition (Ml-Gsvd) With Application To Coordinated Beamforming In Multi-User Mimo Systems
In this paper, we propose a new Multilinear Generalized Singular Value Decomposition (ML-GSVD) which allows to jointly factorize a set of matrices with one common dimension. The ML-GSVD is an extension of the Generalized Singular Value Decomposition (GSVD
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Wind: Wasserstein Inception Distance For Evaluating Generative Adversarial Network Performance
In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Fr?chet Inception Distance (FID),
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Gci Detection From Raw Speech Using A Fully-Convolutional Network
Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithm
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Oh, Jeez! Or Uh-Huh? A Listener-Aware Backchannel Predictor On Asr Transcriptions
This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a proactive listener by inserting backchannels, such as continuers and asses
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Graphtts: Graph-To-Sequence Modelling In Neural Text-To-Speech
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The en
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Indylstms: Independently Recurrent Lstms
We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e. the output and state of each LSTM cell depend
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Bipartite Belief Propagation Polar Decoding With Bit-Flipping
For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, t
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High-Resolution Attention Network With Acoustic Segment Model For Acoustic Scene Classification
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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Low-Complexity Accurate Mmwave Positioning For Single-Antenna Users Based On Angle-Of-Departure And Adaptive Beamforming
The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions in addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable the
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Analysis Of Acoustic Features For Speech Sound Based Classification Of Asthmatic And Healthy Subjects
Non-speech sounds (cough, wheeze) are typically known to perform better than speech sounds for asthmatic and healthy subject classification. In this work, we use sustained phonations of speech sounds, namely, /A:/, /i:/, /u:/, /eI/, /oU/, /s/, and /z/ fro
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Context And Uncertainty Modeling For Online Speaker Change Detection
Speaker change detection is often addressed as a key component in speaker diarization systems. In this work we focus on online speaker change detection as a standalone task which is required for online closed captioning of broadcast television. Contrary t
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Differentially Modulated Spectrally Efficient Frequency-Division Multiplexing
This letter proposes a differentially modulated non-orthogonal spectrally efficient frequency-division multiplexing (D-SEFDM) architecture, which allows us to dispense with any pilot overhead needed for channel estimation at the receiver, while increasing
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Speech Enhancement Using A Two-Stage Network For An Efficient Boosting Strategy
A novel neural network architecture, called two-stage network (TSN), with a multi-objective learning (MOL) method for an efficient boosting strategy (BS) is proposed for speech enhancement. BS is an ensemble method using multiple base predictions (MBPs) f
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Real-Time Sound Event Detection On The Edge: Porting Vggish On Low-Power Iot Microcontrollers
Internet of Things (IoT) applications typically require a large number of heterogeneous devices to be distributed in the environment, which can generate large amounts of data for wireless transmission, affecting the energy requirements and lifetime of the
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Learning To Transfer Multi-Speaker Emotional Prosody To A Neutral Speaker
Most recent emotional speech synthesizers have been studied with a large training data. These systems require a sufficient number of audios to be recorded with respect to different emotions for each speaker. Acquiring emotional speech is more expensive th
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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
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Estimating The Degree Of Sleepiness By Integrating Articulatory Feature Knowledge In Raw Waveform Based Cnns
Speech-based degree of sleepiness estimation is an emerging research problem. This paper investigates an end-to-end approach, where given raw waveform as input, a convolutional neural network (CNN) estimates at its output the degree of sleepiness. Within
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Triplet Loss Feature Aggregation For Scalable Hash
The increasing demands of high resolution and quality aggravate the status of heavy burden of cluster storage side and restricted bandwidth resources. Hence, video de-duplication in storage and transmission is becoming an important feature for video cloud
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Sequential Semi-Orthogonal Multi-Level Nmf With Negative Residual Reduction For Network Embedding
Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed mu
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Competitive methods for lossless screen content coding are based on modelling of probability distributions. The most effective approach for losslessly compressing images with up to 90000 colours is known as `soft context formation' (SCF). It scans the ima
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Ensemble Network For Ranking Images Based On Visual Appeal
We propose a computational framework for ranking images (group photos) taken at the same event within a short time span. The ranking is expected to correspond with human perception of overall appeal of the images. We hypothesize (and provide evidence thro
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A Framework For The Robust Evaluation Of Sound Event Detection
This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based event decisions, event F-scores and event error rates. The proposed framework
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Compressing Flow Fields With Edge-Aware Homogeneous Diffusion Inpainting
In spite of the fact that efficient compression methods for dense two-dimensional flow fields would be very useful for modern video codecs, hardly any research has been performed in this area so far. Our paper addresses this problem by proposing the first
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Audio Feature Extraction For Vehicle Engine Noise Classification
In this paper we propose a new scheme for vehicle engine noise classification as a more privacy-preserving alternative to classifying vehicles based on video recordings. We establish two scenarios: diesel vs. petrol and heavy goods vehicle vs. personal ca