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Accurate 6D Object Pose Estimation By Pose Conditioned Mesh Reconstruction
Current 6D object pose estimation methods consist of Deep Convolutional Neural Networks fully optimized for a single object but with its architecture standardized among objects with different shapes. In contrast to previous works, we explicitly exploit ea
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Low-Frequency Compensated Synthetic Impulse Responses For Improved Far-Field Speech Recognition
We propose a method for generating low-frequency compensated synthetic impulse responses that improve the performance of far-field speech recognition systems trained on artificially augmented datasets. We design linear-phase filters that adapt the simulat
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Channel Charting: An Euclidean Distance Matrix Completion Perspective
Channel charting (CC) is an emerging machine learning framework that aims at learning lower-dimensional representations of the radio geometry from collected channel state information (CSI) in an area of interest, such that spatial relations of the represe
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Libri-Adapt: A New Speech Dataset For Unsupervised Domain Adaptation
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Adapt contains 7200 hours of English speech recorded on mobile and embedded-sc
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Label Reuse For Efficient Semi-Supervised Learning
In this paper, we propose a new learning strategy for semi-supervised deep learning algorithms, called label reuse, aiming to significantly reduce the expensive computational cost of pseudo label generation and the like for each unlabeled training instanc
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End-To-End Non-Negative Autoencoders For Sound Source Separation
Discriminative models for source separation have recently been shown to produce impressive results. However, when operating on sources outside of the training set, these models can not perform as well and are cumbersome to update. Classical methods like N
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Retrieving Vocal-Tract Resonance And Anti-Resonance From High-Pitched Vowels Using A Rahmonic Subtraction Technique
Vocal tract resonances give rise to core spectral information of speech signals. Linear prediction and cepstral methods are widely used for this purpose. However, both approaches are prone to fail as the fundamental frequency (F0) rises. In this study, a
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Passive Intelligent Surface Assisted Mimo Powered Sustainable Iot
Lately, Passive Intelligent Surfaces (PIS) are being recognized to play an important role in meeting the timely demand of low-cost green sustainable Internet of Things (IoT). In this paper, we focus on maximizing the sum received power among the energy ha
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End-To-End Code-Switching Tts With Cross-Lingual Language Model
Code-switching text-to-speech (TTS) aims to enable a system to speak two languages with a single voice and in the same utterance. In this paper, we propose to incorporate cross-lingual word embedding into an end-to-end TTS system, to improve the voice ren
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Lai-Net: Local-Ancestry Inference With Neural Networks
Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in personalized DNA sequence analysis with a
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Atomic Norm Based Localization Of Far-Field And Near-Field Signals With Generalized Symmetric Arrays
Most localization methods for mixed far-field (FF) and nearfield (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generaliz
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Training Keyword Spotters With Limited And Synthesized Speech Data
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in the model creation process is obta
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Classify And Explain: An Interpretable Convolutional Neural Network For Lung Cancer Diagnosis
The deep network-based computer-aided diagnosis systems have encountered many difficulties in practical applications because of its "black box" feature. The crux of the problem is that these models should be explainable ? the model should provide doctors
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Foreground Signature Extraction For An Intimate Mixing Model In Hyperspectral Image Classification
The hyperspectral unmixing problem arises in remote sensing, chemometrics, and biomedical engineering applications. The spectral signature of a single pixel in a hyperspectral cube can be represented as a non-negative combination of non-negative signature
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A Hierarchical Tracker For Multi-Domain Dialogue State Tracking
The goal of Dialogue State Tracking (DST) is to estimate the current dialogue state given all the preceding conversation. Due to the increased number of state candidates, data sparsity problem is still a major hurdle for multi-domain DST. Existing methods
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Frequency Diverse Array Radar: A Closed-Form Solution To Design Weights For Desired Beampattern
In contrast to phased-array radar, frequency-diverse-array (FDA) radar transmits signals of linearly increasing frequencies across the array. As a consequence, the beampattern of an FDA radar becomes range, angle, and time dependent, which is different fr
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M-Estimators Of Scatter With Eigenvalue Shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is consid
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Speech Intelligibility Enhancement By Equalization For In-Car Applications
In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase in
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Content Vs Context: How About "walking Hand-In-Hand" For Image Clustering?
Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the power
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Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging
This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on trainin
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On End-To-End Multi-Channel Time Domain Speech Separation In Reverberant Environments
This paper introduces a new method for multi-channel time domain speech separation in reverberant environments. A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings, with no need of c
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Portfolio Cuts: A Graph-Theoretic Framework To Diversification
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis
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Hi-Mia : A Far-Field Text-Dependent Speaker Verification Database And The Baselines
This paper presents a large far-field text-dependent speaker verification database named HI-MIA. We aim to meet the data requirement for far-field microphone array based speaker verification since most of the publicly available databases are single channe
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Characterization Of A Snapshot Fourier Transform Imagingspectrometer Based On An Array Of Fabry-Perot Interferometers
This study focuses on a novel snapshot Fourier Transform imaging spectrometer based on an array of Fabry-Perot interferometers. This device fully relies on signal processing in order to provide intelligible outputs and thus requires a precise characterisa
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Maximally Energy-Concentrated Differential Window For Phase-Aware Signal Processing Using Instantaneous Frequency
The short-time Fourier transform (STFT) is widely employed in nonstationary signal analysis, whose property depends on window functions. Instantaneous frequency in STFT, the time-derivative of phase, is recently applied to many applications including spec
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Deep Multi-Region Hashing
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data w
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Beam Elimination Based On Sequentially Estimated A Posteriori Probabilities Of Winning
A robust and adaptive variable length beam selection strategy based on M-ary sequential competition was proposed in [1]. It was enhanced by the elimination of inauspicious beams during the ongoing competition to improve the efficiency and speed of the tra
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Deep neural networks (DNNs) are successful in applications with matching inference and training distributions. In real-world scenarios, DNNs have to cope with truly new data samples during inference, potentially coming from a shifted data distribution. Th
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Multiple Points Input For Convolutional Neural Networks In Replay Attack Detection
The models based on convolutional neural network (CNN) have shown remarkable performance in spoofing detection for automatic speaker verification. In order to input data into CNN-based models in mini-batch unit, the shape of all data in each mini-batch mu
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Improving Auditory Attention Decoding Performance Of Linear And Non-Linear Methods Using State-Space Model
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using aud
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Meta-Learning To Communicate: Fast End-To-End Training For Fading Channels
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach
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Lipreading Using Temporal Convolutional Networks
Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU)
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Improving Cross-Dataset Performance Of Face Presentation Attack Detection Systems Using Face Recognition Datasets
Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under
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Orthogonal Training For Text-Independent Speaker Verification
In this paper we propose orthogonal training schemes to improve the effectiveness of cosine similarity measurements in text-independent speaker verification (SV) tasks. Compared to the PLDA backend, cosine similarity is simple to compute, and it does not
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Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous approaches only exp
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On The Choice Of Graph Neural Network Architectures
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sig
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Griffin–Lim Like Phase Recovery Via Alternating Direction Method Of Multipliers
Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing. When only an amplitude spectrogram is available and no explicit information is given for the phases, the Griffin-Lim algorithm
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Bilevel Optimization Using Stationary Point Of Lower-Level Objective Function
In this letter, we address an audio signal separation problem and propose a new effective algorithm for solving a bilevel optimization in discriminative nonnegative matrix factorization (NMF). Recently, discriminative training of NMF bases has been develo
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Deepjscc: The Future Of Wireless Video Transmission
We propose a demonstration of a joint source-channel coding (JSCC) scheme, called DeepJSCC, for wireless video transmission. Unlike conventional digital communication systems, which rely on separate source and channel coding, DeepJSCC is a purely data-dri
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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