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Showing 1901 - 1950 of 1951
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Subjective Quality Estimation Using Pesq For Hands-Free Terminals
Previous reports have mentioned the possibility that subjective quality of the echo-suppressed speech signal can be estimated based on perceptual evaluation of speech quality (PESQ), but there are few experimental results. We propose third-party listening
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Prediction Of Voicing And The F0 Contour From Electromagnetic Articulography Data For Articulation-To-Speech Synthesis
Articulation-to-speech synthesis based solely on supraglottal articulation requires some sort of intonation control. This paper examines to what extent the f0 contour of an utterance can be predicted from such supraglottal articulation data. To that end,
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Bayesian Multiple Change-Point Detection With Limited Communication
Several modern applications involve large-scale sensor networks for statistical inference. For example, such sensor networks are of significant interest for Internet of Things applications. In this paper, we consider Bayesian multiple change-point detecti
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Hand-3D-Studio: A New Multi-View System For 3D Hand Reconstruction
This paper proposes a new system named as Hand-3D-Studio to capture the 3D hand pose and shape information. Our system includes 15 synchronized DSLR cameras, which can acquire high quality multi-view 4K resolution color images in a circular manner. We the
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Source Enumeration Via Toeplitz Matrix Completion
This paper addresses the problem of source enumeration by an array of sensors in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances, referred to non-iid noise hereafter, when the sources are unc
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Multimodal Transformer Fusion For Continuous Emotion Recognition
Multimodal fusion increases the performance of emotion recognition because of the complementarity of different modalities. Compared with decision level and feature level fusion, model level fusion makes better use of the advantages of deep neural networks
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End-To-End Training Of Time Domain Audio Separation And Recognition
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multispeaker speech recognition. However, up until now, state-of-the-art neural network?based time domain source separation has not
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Interrupted And Cascaded Permutation Invariant Training For Speech Separation
Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem. With PIT selecting the minimum cost label assignments dynamically, very few studies considered the sep
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Dnn-Based Distributed Multichannel Mask Estimation For Speech Enhancement In Microphone Arrays
Multichannel processing is widely used for speech enhancement but several limitations appear when trying to deploy these solutions in the real world. Distributed sensor arrays that consider several devices with a few microphones is a viable solution which
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Performance Analysis For Path Attenuation Estimation Of Microwave Signals Due To Rainfall And Beyond
The attenuation of microwave signals can be used for meteorological observations. For example, the received signal level (RSL) of backhaul links of cellular systems, which usually has quantization error of 0.1 dB or more for commercial systems, has been u
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A Bidirectional Context Propagation Network For Urine Sediment Particle Detection In Microscopic Images
The microscopic urine sediment examination is a crucial part in the evaluation of renal and urinary tract diseases. Recently, there are emerging CNNs-based detectors to detect the urine sediment particles in an end-to-end manner. However, it is not very c
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Hierarchical Attention Transfer Networks For Depression Assessment From Speech
A growing area of mental health research is the search for speech-based objective markers for conditions such as depression. However, when combined with machine learning, this search can be challenging due to a limited amount of annotated training data. I
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Group-Utility Metric For Efficient Sensor Selection And Removal In Lcmv Beamformers
In sensor arrays or sensor networks, tracking each sensor?s utility helps in excluding those which do not sufficiently contribute to the task at hand, thereby reducing energy consumption or avoiding model overfitting. In a linearly-constrained minimum var
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Optimal Sampling Rate And Bandwidth Of Bandlimited Signals - An Algorithmic Perspective
The bandwidth of a bandlimited signal is a key quantity that is relevant in numerous applications. For example, it determines the minimum sampling rate that is necessary to reconstruct a bandlimited signal from its samples. In this paper we study if it is
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Learning Spatio-Temporal Representations With Temporal Squeeze Pooling
In this paper, we propose a new video representation learning method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set of few images, named Squeezed Images
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Improving Sample-Efficiency In Reinforcement Learning For Dialogue Systems By Using Trainable-Action-Mask
By interacting with human and learning from reward signals, reinforcement learning is an ideal way to build conversational AI. Concerning the expenses of real-users' responses, improving sample-efficiency has been the key issue when applying reinforcement
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Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis
Depression is a mental illness that may be harmful to an individual?s health. Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection. Typically, depre
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Cross Lingual Transfer Learning For Zero-Resource Domain Adaptation
We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual mode
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Joint Scheduling And Beamforming For Delay Sensitive Traffic With Priorities And Deadlines
Packet scheduling in 5G networks can significantly affect the perfor- mance of beamforming techniques since the allocation of multiple users to the same time-frequency block causes interference between users. A combination of beamforming and scheduling ca
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Self-Driven Graph Volterra Models For Higher-Order Link Prediction
Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-orde
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Blind Bounded Source Separation Using Neural Networks With Local Learning Rules
An important problem encountered by both natural and engineered signal processing systems is blind source separation. In many instances of the problem, the sources are bounded by their nature and known to be so, even though the particular bound may not be
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Robust Covariance Matrix Estimation And Portfolio Allocation: The Case Of Non-Homogeneous Assets
This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same improvem
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A Deep Learning Approach To Object Affordance Segmentation
Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting the object parts that enable specific types of human-object interaction, the
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Alignment-Length Synchronous Decoding For Rnn Transducer
We present a beam decoding strategy for recurrent neural network transducers which has the characteristic that all competing hypotheses within the beam have the same alignment length (number of output symbols plus BLANK symbols). We contrast the proposed
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Generalized Spatial Modulation For Wireless Terabits Systems Under Sub-Thz Channel With Rf Impairments
Multiple-Input Multiple-Output (MIMO) technique with Index Modulation (IM) over sub-TeraHertz (sub-THz) bands represent a promising solution to design new wireless ultra-high data rate systems. However, the system design over sub-THz bands suffers from ma
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Towards Real-Time Single-Channel Singing-Voice Separation With Pruned Multi-Scaled Densenets
Modern musical source separation systems based on deep neural networks reach unprecedented levels of separation quality. However, harnessing the power of these large-scale models in typical audio production environments, which frequently offer only limite
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Hierarchical Federated Learning Across Heterogeneous Cellular Networks
We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively learn a global model by sharing local updates on the model parameters rather than their datasets, with the help of a mobile base station (MBS). We optimize the resource a
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Machine learning approaches to auditory object recognition are traditionally based on engineered features such as those derived from the spectrum or cepstrum. More recently, end- to-end classification systems in image and auditory recognition systems have
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Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognition
We propose a novel approach to semi-supervised automatic speech recognition (ASR). We first exploit a large amount of unlabeled audio data via representation learning, where we reconstruct an unseen temporal slice of filterbank features from past and futu
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Modeling Uncertainty In Predicting Emotional Attributes From Spontaneous Speech
A challenging task in affective computing is to build reliable speech emotion recognition (SER) systems that can accurately predict emotional attributes from spontaneous speech. To increase the trust in these SER systems, it is important to predict not on
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Modeling Behavioral Consistency In Large-Scale Wearable Recordings Of Human Bio-Behavioral Signals
Continuously-worn wearable sensors provide an unprecedented opportunity to unobtrusively measure rich bio-behavioral time-series recordings in natural settings such as the workplace. These time-series data can be helpful in inferring broad patterns of beh
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Application Informed Motion Signal Processing For Finger Motion Tracking Using Wearable Sensors
Finger motion tracking has applications in user-interfaces, sports analytics, medical rehabilitation and sign language translation. This paper presents a system called FinGTrAC that shows the feasibility of fine grained finger gesture tracking using low i
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High-Accuracy And Low-Latency Speech Recognition With Two-Head Contextual Layer Trajectory Lstm Model
While the community keeps promoting end-to-end models over conventional hybrid models, which usually are long short-term memory (LSTM) models trained with a cross entropy criterion followed by a sequence discriminative training criterion, we argue that su
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A New Variational Method For Deep Supervised Semantic Image Hashing
We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing the
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From Symbols To Signals: Symbolic Variational Autoencoders
We introduce Symbolic Variational Autoencoders which generate images from symbols that represent semantic concepts. Unlike generic Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), the latent distribution from the Symbolic Variati
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Low-Complexity Fixed-Point Convolutional Neural Networks For Automatic Target Recognition
There has been growing interest in developing neural network based automatic target recognition systems for synthetic aperture radar applications. However, these networks are typically complex in terms of storage and computation which inhibits their deplo
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Learning Multi-Scale Attentive Features For Series Photo Selection
People used to take a series of nearly identical photos about the same subject, but it is usually a tedious chore to select the reversed ones from them. Despite the remarkable progress, most existing studies on image aesthetics assessment fail to fulfill
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End-To-End Generation Of Talking Faces From Noisy Speech
Acoustic cues are not the only component in speech communication; if the visual counterpart is present, it is shown to benefit speech comprehension. In this work, we propose an end-to-end (no pre- or post-processing) system that can generate talking faces
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A Study On The Transferability Of Adversarial Attacks In Sound Event Classification
An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is
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An Alternative Signature Design Using L1 Principal Components For Spread-Spectrum Steganography
As methods for detecting hidden data evolve, there exits an ever increasing need to develop new steganographic solutions. This paper introduces novel spread spectrum (SS) and improved spread spectrum (ISS) multimedia data embedding techniques using L_1 pr
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Single Frequency Filter Bank Based Long-Term Average Spectra For Hypernasality Detection And Assessment In Cleft Lip And Palate Speech
Hypernasality is an abnormality in speech production observed in subjects with craniofacial anomalies like cleft lip and palate (CLP). Detection and assessment of hypernasality is a primary step in the clinical diagnosis of individuals with CLP. Existing
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Fg2Seq: Effectively Encoding Knowledge For End-To-End Task-Oriented Dialog
End-to-end Task-oriented spoken dialog systems typically require modeling two types of inputs, namely, the dialog history which is a sequence of utterances and the knowledge base (KB) associated with the dialog history. While modeling these inputs, curren
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Tensorflow Audio Models In Essentia
Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexib
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Enhancing End-To-End Multi-Channel Speech Separation Via Spatial Feature Learning
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. However, these manually designed spatial features are hard to incorporate into
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Joint Beamforming And Reverberation Cancellation Using A Constrained Kalman Filter With Multichannel Linear Prediction
The performance of speech processing systems degrades significantly in far-field scenarios where the distance between the user and microphones increases, leading to low signal-to-noise and signal-to-reverberation ratios. To address this challenge, combini
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Trapezoidal Segment Sequencing: A Novel Approach For Fusion Of Human-Produced Continuous Annotations
Generating accurate ground truth representations of human subjective experiences and judgements is essential for advancing our understanding of human-centered constructs such as emotions. Often, this requires the collection and fusion of annotations from
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Classification Of Epileptic Ieeg Signals By Cnn And Data Augmentation
Epileptic focus localization in patients with epileptic seizures is essential when surgery is needed. Recent studies show that this can be done automatically using machine learning approaches. However, well-designed feature extraction methods are often co
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Efficient And Scalable Neural Residual Waveform Coding With Collaborative Quantization
Scalability and efficiency are desired in neural speech codecs, which supports a wide range of bitrates for applications on various devices. We propose a collaborative quantization (CQ) scheme to jointly learn the codebook of LPC coefficients and the corr
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Learning A Subword Inventory Jointly With End-To-End Automatic Speech Recogntion
Recent work has demonstrated the promise of using subword units as output targets for sequence-to-sequence automatic speech recognition (ASR) models. Our work builds on the latent sequence decomposition (LSD) framework, in which the use of subword units f