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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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We propose and evaluate transformer-based acoustic models for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional embedding methods and an iterated loss to enable training deep transformers. We also
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Speaker Embeddings Incorporating Acoustic Conditions For Diarization
We present our work on training speaker embeddings, especially effective for speaker diarization. For various speaker recognition tasks, extracting speaker embeddings using Deep Neural Networks (DNNs) has become major methods. These embeddings are general
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Learning Perception And Planning With Deep Active Inference
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we
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Hearing Aid Research Data Set For Acoustic Environment Recognition
State-of-the-art hearing aids (HA) are limited in recognizing acoustic environments. Much effort is spent on research to improve listening experience for HA users in every acoustic situation. There is, however, no dedicated public database to train acoust
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Recurrent Neural Audiovisual Word Embeddings For Synchronized Speech And Real-Time Mri
In this paper, the use of word embeddings for the segments found in audio and real-time magnetic resonance imaging (rtMRI) videos is addressed. In this study, word embeddings are created to store and retrieve data efficiently, and their representation pow
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1.5Gbit/S 4.9W Hyperspectral Image Encoders On A Low-Power Parallel Heterogeneous Processing Platform
This work explores the utilization of low-power heterogeneous devices for parallelizing the compute-intensive hyper-spectral and multispectral image compression CCSDS-123 entropy encoders. Multithread processing allows for the near-optimal system?s bandwi
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Improving Music Transcription By Pre-Stacking A U-Net
We propose to pre-stack a U-Net as a way of improving the polyphonic music transcription performance of various baseline Convolutional Neural Networks (CNNS). The U-Net, a network architecture based on skip-connections between layers acts as a transformat
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Privacy Aware Acoustic Scene Synthesis Using Deep Spectral Feature Inversion
Gathering information about the acoustic environment of urban areas is now possible and studied in many major cities in the world. Part of the research is to find ways to inform the citizen about its sound environment while ensuring her privacy. We study
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Cloud-Driven Multi-Way Multiple-Antenna Relay Systems: Best-User-Link Selection And Joint Mmse Detection
In this work, we present a cloud-driven uplink framework for multi-way multiple-antenna relay systems which facilitates joint linear Minimum Mean Square Error (MMSE) symbol detection in the cloud and where users are selected to simultaneously transmit to
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Sparse Branch And Bound For Exact Optimization Of L0-Norm Penalized Least Squares
We propose a global optimization approach to solve l_0-norm penalized least-squares problems, using a dedicated branch-and-bound methodology. A specific tree search strategy is built, with branching rules inspired from greedy exploration techniques. We sh
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An Lstm Based Architecture To Relate Speech Stimulus To Eeg
Modeling the relationship between natural speech and a recorded electroencephalogram (EEG) helps us understand how the brain processes speech and has various applications in neuroscience and brain-computer interfaces. In this context, so far mainly linear
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A Composite Dnn Architecture For Speech Enhancement
In speech enhancement, the use of supervised algorithms in the form of deep neural networks (DNNs) has become tremendously popular in recent years. The target function of the DNN (and the associated estimators) is often either a masking function applied t
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Low-Rank Gradient Approximation For Memory-Efficient On-Device Training Of Deep Neural Network
Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models. However, one of the major obstacles to achieving this goal is the memory limitation of mobile devices. Reducing training memory enab
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Image De-Raining Via Rdl: When Reweighted Convolutional Sparse Coding Meets Deep Learning
Over the past few decades, image de-raining has witnessed substantial progress due to the development of priors and deep learning based methods. However, few studies combine the merits of both. In this paper, we argue that domain expertise of conventional
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Minimum Latency Training Strategies For Streaming Sequence-To-Sequence Asr
Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. However, in these models, the decisions to generate tokens are delayed compared
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Lqaid: Localized Quality Aware Image Denoising Using Deep Convolutional Neural Networks
In this paper we propose the Localized Quality Aware Image Denoising (LQAID) technique for image denoising using deep convolutional neural networks (CNNs). LQAID relies on local quality estimates over global cues like noise standard deviation since the pe
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Decomposed Cyclegan For Single Image Deraining With Unpaired Data
Most previous learning-based methods required paired rain image data. In practice, however, paired rain data cannot be collected. Inspired by adopting unpaired data in task of translation, in this paper we present a new method for rain removal using unpai
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One-Bit Normalized Scatter Matrix Estimation For Complex Elliptically Symmetric Distributions
One-bit quantization has attracted attention in massive MIMO, radar, and array processing, due to its simplicity, low cost, and capability of parameter estimation. Specifically, the shape of the covariance of the unquantized data can be estimated from the
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Proximal Multitask Learning Over Distributed Networks With Jointly Sparse Structure
Modeling relations between local optimum parameter vectors in multitask networks has attracted much attention over the last years. This work considers a distributed optimization problem for parameter vectors with a jointly sparse structure among nodes, th
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Generating Multilingual Voices Using Speaker Space Translation Based On Bilingual Speaker Data
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We present progress towards bilingual Text-to-Speech which is able to transform a monolingual voice to speak a second language while preserving speaker voice quality. We demonstrate that a bilingual speaker embedding space contains a separate distribution
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Neural Percussive Synthesis Parameterised By High-Level Timbral Features
We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds withou
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Robustness Of Sparse Bayesian Learning In Correlated Environments
In this paper we explore the robustness of Sparse Bayesian Learning (SBL) in an environment with correlated sources. We provide two new perspectives to understand SBL's strategy for handling correlated sources. Using a Minimum Power Distortionless Respons
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End-To-End Multi-Person Audio/Visual Automatic Speech Recognition
Traditionally, audio-visual automatic speech recognition has been studied under the assumption that the speaking face on the visual signal is the face matching the audio. However, in a more realistic setting, when multiple faces are potentially on screen
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Bband Index: A No-Reference Banding Artifact Predictor
Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can be very noticeable in high-definition videos. Here we study this artifact,
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Gender Differences On The Perception And Production Of Utterances With Willingness And Reluctance In Chinese
This study intends to explore the effects of gender differences on the perception and production of emotional intonation with willingness and reluctance. In the perceptual study, 20 native Mandarin listeners were instructed to rate perceived degree of wil
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Experiments In Creating Online Course Content For Signal Processing Education
The creation of the NPTEL platform in India has led to a vast population of engineering students getting access to quality online content for Signal Processing. These courses are globally accessible, free of cost, and also provide a means of obtaining cer
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Gait Phase Segmentation Using Weighted Dynamic Time Warping And K-Nearest Neighbors Graph Embedding
Gait phase segmentation is the process of identifying the start and end of different phases within a gait cycle. It is essential to many medical applications, such as disease diagnosis or rehabilitation. This work utilizes inertial measurement units (IMUs
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Towards Blind Quality Assessment Of Concert Audio Recordings Using Deep Neural Networks
Live music audio and video recordings represent a large percentage of the huge amount of User Generated Content (UGC) that is available on the internet today. Applications and services related to the management and consumption of this content may signific
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On The Limit Distribution Of The Canonical Correlation Coefficients Between The Past And The Future Of A High-Dimensional White Noise
It is shown that the distribution of the estimated canonical correlation coefficients between the past and the future of a high-dimensional multivariate white noise sequence converges almost surely towards a limit distribution whose density is given in cl
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Weight Sharing And Deep Learning For Spectral Data
We propose a novel method to co-train deep convolutional neural networks for data sets of differing position specific data. This is an advantage in chemometrics where individual measurements represent exact chemical compounds, e.g. for given wavelengths,
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Pathloss Prediction Using Deep Learning With Applications To Cellular Optimization And Efficient D2D Link Scheduling
In this paper we propose a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane. Our method, termed RadioUNet, is a deep neural network. For applications such as user-cell site as
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Audio-Based Detection Of Explicit Content In Music
We present a novel automatic system for performing explicit content detection directly on the audio signal. Our modular approach uses an audio-to-character recognition model, a keyword spotting model associated with a dictionary of carefully chosen keywor
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Ordinal Learning For Emotion Recognition In Customer Service Calls
Approaches toward ordinal speech emotion recognition (SER) tasks are commonly based on the categorical classification algorithms, where the rank-order emotions are arbitrarily treated as independent categories. To employ the ordinal information between em