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Unsupervised Training For Deep Speech Source Separation With Kullback-Leibler Divergence Based Probabilistic Loss Function
In this paper, we propose a multi-channel speech source separation method with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an alternative to a clean signal, the proposed method adopts an estimated
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A Deep Gradient Boosting Network For Optic Disc And Cup Segmentation
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-ofthe-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connect
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Feature Selection Under Orthogonal Regression With Redundancy Minimizing
Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square
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Audio Codec Enhancement With Generative Adversarial Networks
Audio codecs are typically transform-domain based and efficiently code stationary audio signals, but they struggle with speech and signals containing dense transient events such as applause. Specifically, with these two classes of signals as examples, we
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Differentiable Branching In Deep Networks For Fast Inference
In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them con
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Cross Image Cubic Interpolator For Spatially Varying Exposures
Spatially varying exposures via rolling shutter is an efficient way to capture differently exposed images for high dynamic range (HDR) scenes. Neither camera movement nor moving objects is an issue for such a captured method. However, a possible issue is
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Beyond The Dcase 2017 Challenge On Rare Sound Event Detection: A Proposal For A More Realistic Training And Test Framework
There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more
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Lie Group State Estimation Via Optimal Transport
Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints.
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Distributed Quantization For Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of
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Maximum Likelihood Estimation Of The Interference-Plus-Noise Cross Power Spectral Density Matrix For Own Voice Retrieval
In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross power spectral density (CPSD) of the noise is required. In this paper, a novel maximum
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Multitask Learning And Multistage Fusion For Dimensional Audiovisual Emotion Recognition
Due to its ability to accurately predict emotional state using multimodal features, audiovisual emotion recognition has recently gained more interest from researchers. This paper proposes two methods to predict emotional attributes from audio and visual d
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Joint Resource Allocation And Routing For Service Function Chaining With In-Subnetwork Processing
Network Function Virtualization (NFV) is an efficient approach to simplify and accelerate the deployment of diverse network services. A critical challenge lies in mapping Virtual Network Functions (VNFs) to high-volume servers, resource allocation, and tr
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On-The-Fly Feature Selection And Classification With Application To Civic Engagement Platforms
Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framewo
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Mutual-Information-Based Sensor Placement For Spatial Sound Field Recording
A sensor (microphone) placement method based on mutual information for spatial sound field recording is proposed. The sound field recording methods using distributed sensors enable the estimation of the sound field inside a target region of arbitrary shap
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Dynamic Variational Autoencoders For Visual Process Modeling
This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector autoregressive model an
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Exploiting Rays In Blind Localization Of Distributed Sensor Arrays
Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i.e. t
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Opendenoising: An Extensible Benchmark For Building Comparative Studies Of Image Denoisers
Image denoising has recently taken a leap forward due to machine learning. However, image denoisers, both expert-based and learning-based, are mostly tested on well-behaved generated noises (usually Gaussian) rather than on real-life noises, making perfor
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Adaptive Blind Audio Source Extraction Supervised By Dominant Speaker Identification Using X-Vectors
We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised
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Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room fo
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Vamp With Vector-Valued Diagonalization
Vector approximate message passing is studied where vector-valued diagonalization instead of a uniform one is employed. Thereby, individual variances are tracked within the algorithm instead of an average one. Straightforward application based on the expe
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Sparse Directed Graph Learning For Head Movement Prediction In 360 Video Streaming
High-definition 360 videos encoded in fine quality are typically too large to stream in its entirety over bandwidth (BW)-constrained networks. One popular remedy is to extract and send a spatial sub-region corresponding to a viewer's current field-of-view
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Bandwidth Extension Of Musical Audio Signals With No Side Information Using Dilated Convolutional Neural Networks
Bandwidth extension has a long history in audio processing. While speech processing tools do not rely on side information, production-ready bandwidth extension tools of general audio signals rely on side information that has to be transmitted alongside th
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Cif: Continuous Integrate-And-Fire For End-To-End Speech Recognition
In this paper, we propose a novel soft and monotonic alignment mechanism used for sequence transduction. It is inspired by the integrate-and-fire model in spiking neural networks and employed in the encoder-decoder framework consists of continuous functio
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Gray-Scale Image Colorization Using Cycle-Consistent Generative Adversarial Networks With Residual Structure Enhancer
The colorization of gray-scale images has always been a challenging task in computer vision. Recently, novel approaches have been introduced for unsupervised image translation between two domains using Generative Adversarial Networks (GANs). Since one can
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Inferring Dynamic Group Leadership Using Sequential Bayesian Methods
In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for i
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Audio-Visual Recognition Of Overlapped Speech For The Lrs2 Dataset
Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of audio-visual technologies for overlapped speech recognition. Three issues
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A Proximal Dual Consensus Method For Linearly Coupled Multi-Agent Non-Convex Optimization
Motivated by large-scale signal processing and machine learning applications, this paper considers the distributed multi-agent optimization problem for a linearly constrained non-convex problem. Each of the agents owns a local cost function and local vari
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Extracting Unit Embeddings Using Sequence-To-Sequence Acoustic Models For Unit Selection Speech Synthesis
This paper presents a method of using the intermediate representations between linguistic and acoustic features in a Tacotron model to derive the cost functions for unit selection speech synthesis. By extracting the outputs of the Tacotron encoder, each p
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Non-Local Nested Residual Attention Network For Stereo Image Super-Resolution
Nowadays CNN-based stereo image super-resolution(SR) methods have obtained remarkable performance. However, most of existing methods only superficially portrayed the low layer features without considering the uneven distribution of information, which is i
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Bandit Sampling For Faster Activity And Data Detection In Massive Random Access
This paper considers the grant-free random access scheme in IoT networks with a massive number of devices that are sporadically active. By embedding the data symbols in the signature sequences, joint device activity detection, and data decoding can be ach
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Similarity Learning For Cover Song Identification Using Cross-Similarity Matrices Of Multi-Level Deep Sequences
In recent years, several deep learning models have been proposed for cover song identification and they have been designed to learn fixed-length feature vectors for music tracks. However, the aspect of temporal progression of music, which is important for
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Meta Learning For End-To-End Low-Resource Speech Recognition
In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to
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Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convol
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Audio-Visual Calibration With Polynomial Regression For 2-D Projection Using Svd-Phat
This paper proposes a straightforward 2-D method to spatially calibrate the visual field of a camera with the auditory field of an array microphone by generating and overlaying an acoustic image over an optical image. Using a low-cost microphone array and
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Feature Affine Projection Algorithms
There is a growing research interest in proposing new techniques to detect and exploit signals/systems sparsity. Recently, the idea of hidden sparsity has been proposed, and it has been shown that, in many cases, sparsity is not explicit, and some tools a
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Doa Estimation In Systems With Nonlinearities For Mmwave Communications
Accurate and efficient methods for Direction of Arrival (DOA) estimation play an important role in mmWave channel estimation methods. This estimation procedure can potentially be affected by the different RF and analog components in the communication syst
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Perception-Distortion Trade-Off With Restricted Boltzmann Machines
In this work, we introduce a new procedure for applying Restricted Boltzmann Machines (RBMs) to missing data inference tasks, based on linearization of the effective energy function governing the distribution of observations. We compare the performance of
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Evaluation Of Joint Auditory Attention Decoding And Adaptive Binaural Beamforming Approach For Hearing Devices With Attention Switching
Beamforming is a common technique used to improve speech intelligibility and listening comfort of hearing aids users in a noisy environment. Traditional hearing aids beamforming algorithms require the a priori knowledge of the auditory attention of the li
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Using X-Vectors To Automatically Detect Parkinson's Disease From Speech
The promise of new neuroprotective treatments to stop or slow the advance of Parkinson's Disease (PD) urges for new biomarkers or detection schemes that can deliver a faster diagnosis. Given that speech is affected by PD, the combination of deep neural ne
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Few-Shot Acoustic Event Detection Via Meta Learning
We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data and facilitates personalization of AED systems for users in real applications. Compared to other research area
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Improving Voice Separation By Incorporating End-To-End Speech Recognition
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data. In this work, we propose to explicitly incorporate the phonetic and linguistic nature of speech by
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Distilling Attention Weights For Ctc-Based Asr Systems
We present a novel training approach for connectionist temporal classification (CTC) -based automatic speech recognition (ASR) systems. CTC models are promising for building both a conventional acoustic model and an end-to-end (E2E) ASR model. However, CT
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Estimation Of Post-Nonlinear Causal Models Using Autoencoding Structure
Discovering causal relations in complex systems is an important problem in many research fields. To describe such systems involving nonlinear causal relations, the post-nonlinear (PNL) causal model has been proposed. However, despite its identifiability,
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Robust Low Rate Speech Coding Based On Cloned Networks And Wavenet
Rapid advances in machine-learning based generative modeling of speech make its use in speech coding attractive. However, the current performance of such models drops rapidly with noise contamination of the input, preventing use in practical applications.
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3D Deformation Signature For Dynamic Face Recognition
This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique defo
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An Analysis Of Speech Enhancement And Recognition Losses In Limited Resources Multi-Talker Single Channel Audio-Visual Asr
In this paper, we analyzed how audio-visual speech enhancement can help to perform the ASR task in a cocktail party scenario. Therefore we considered two simple end-to-end LSTM-based models that perform single-channel audiovisual speech enhancement and ph
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Fully-Neural Approach To Heavy Vehicle Detection On Bridges Using A Single Strain Sensor
Bridge weigh-in-motion (BWIM) is a technique for detecting heavy vehicles that may cause serious damage to real bridges. BWIM is realized by analyzing the strain signals observed at places on the bridge in terms of bridge-component responses to the axle l
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Energy Efficient Acceleration Of Floating Point Applications Onto Cgra
In this paper, we propose a novel CGRA architecture and associated compilation flow supporting both integer and floating-point computations for energy efficient acceleration of DSP applications. Experimental results show that the proposed accelerator achi
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Overlapped State Hidden Semi-Markov Model For Grouped Multiple Sequences
Efficient analysis of multiple sequential data is becoming necessary for identifying sequential patterns of multiple objects of interest. This analysis has major practical and technical importance because finding such patterns necessitates extraction and
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Anomalydae: Dual Autoencoder For Anomaly Detection On Attributed Networks
Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection. However, mo