pwc-archive/papers-with-abstracts huggingface dataset:
Towards sound based testing of COVID-19 -- Summary of the first Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge
Authors: Neeraj Kumar Sharma, Ananya Muguli, Prashant Krishnan, Rohit Kumar, Srikanth Raj Chetupalli, Sriram Ganapathy
Date: 2021-06-21 00:00:00
Tasks: Diagnostic

The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the ``Coswara'' project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled ``Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge''. The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-curve of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technology for acoustic-based POCTs.
Speech prosody and remote experiments: a technical report
Authors: Giuseppe Magistro
Date: 2021-06-21 00:00:00

The aim of this paper is twofold. First, we present a review of different recording options for gathering prosodic data in the event that fieldwork is impracticable (e.g. due to pandemics). Under this light, we mimic a long-distance reading task experiment using different software and hardware synchronously. In order to evaluate the employed methodologies, we extract noise levels and frequency manipulation of the recordings. Subsequently, we examine the impact of the different recordings onto linguistic variables, such as the pitch curves and values. We also include a discussion on experimental practicalities. After balancing these factors, we decree an online platform, Zencastr, as the most affordable and practical for acoustic data collection. Secondly, we want to open up a debate on the most optimal remote methodology that researchers on speech prosody can deploy.
Glow-WaveGAN: Learning Speech Representations from GAN-based Variational Auto-Encoder For High Fidelity Flow-based Speech Synthesis
Authors: Jian Cong, Shan Yang, Lei Xie, Dan Su
Date: 2021-06-21 00:00:00
Tasks: Speech Synthesis

Current two-stage TTS framework typically integrates an acoustic model with a vocoder -- the acoustic model predicts a low resolution intermediate representation such as Mel-spectrum while the vocoder generates waveform from the intermediate representation. Although the intermediate representation is served as a bridge, there still exists critical mismatch between the acoustic model and the vocoder as they are commonly separately learned and work on different distributions of representation, leading to inevitable artifacts in the synthesized speech. In this work, different from using pre-designed intermediate representation in most previous studies, we propose to use VAE combining with GAN to learn a latent representation directly from speech and then utilize a flow-based acoustic model to model the distribution of the latent representation from text. In this way, the mismatch problem is migrated as the two stages work on the same distribution. Results demonstrate that the flow-based acoustic model can exactly model the distribution of our learned speech representation and the proposed TTS framework, namely Glow-WaveGAN, can produce high fidelity speech outperforming the state-of-the-art GAN-based model.
Controllable Context-aware Conversational Speech Synthesis
Authors: Jian Cong, Shan Yang, Na Hu, Guangzhi Li, Lei Xie, Dan Su
Date: 2021-06-21 00:00:00
Tasks: Speech Synthesis

In spoken conversations, spontaneous behaviors like filled pause and prolongations always happen. Conversational partner tends to align features of their speech with their interlocutor which is known as entrainment. To produce human-like conversations, we propose a unified controllable spontaneous conversational speech synthesis framework to model the above two phenomena. Specifically, we use explicit labels to represent two typical spontaneous behaviors filled-pause and prolongation in the acoustic model and develop a neural network based predictor to predict the occurrences of the two behaviors from text. We subsequently develop an algorithm based on the predictor to control the occurrence frequency of the behaviors, making the synthesized speech vary from less disfluent to more disfluent. To model the speech entrainment at acoustic level, we utilize a context acoustic encoder to extract a global style embedding from the previous speech conditioning on the synthesizing of current speech. Furthermore, since the current and previous utterances belong to the different speakers in a conversation, we add a domain adversarial training module to eliminate the speaker-related information in the acoustic encoder while maintaining the style-related information. Experiments show that our proposed approach can synthesize realistic conversations and control the occurrences of the spontaneous behaviors naturally.
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection
Authors: Kazuki Shimada, Naoya Takahashi, Yuichiro Koyama, Shusuke Takahashi, Emiru Tsunoo, Masafumi Takahashi, Yuki Mitsufuji
Date: 2021-06-21 00:00:00
Tasks: Data Augmentation, Diversity, Sound Event Localization and Detection

This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference. Our previous system based on activity-coupled Cartesian direction of arrival (ACCDOA) representation enables us to solve a SELD task with a single target. This ACCDOA-based system with efficient network architecture called RD3Net and data augmentation techniques outperformed state-of-the-art SELD systems in terms of localization and location-dependent detection. Using the ACCDOA-based system as a base, we perform model ensembles by averaging outputs of several systems trained with different conditions such as input features, training folds, and model architectures. We also use the event independent network v2 (EINV2)-based system to increase the diversity of the model ensembles. To generalize the models, we further propose impulse response simulation (IRS), which generates simulated multi-channel signals by convolving simulated room impulse responses (RIRs) with source signals extracted from the original dataset. Our systems significantly improved over the baseline system on the development dataset.
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Authors: Shota Horiguchi, Yusuke Fujita, Shinji Watanabe, Yawen Xue, Paola Garcia
Date: 2021-06-20 00:00:00
Tasks: Decoder, speaker-diarization, Speaker Diarization

This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization, EEND methods are better in terms of speaker overlap handling. However, EEND still has a disadvantage in that it cannot deal with a flexible number of speakers. To remedy this problem, we introduce encoder-decoder-based attractor calculation module (EDA) to EEND. Once frame-wise embeddings are obtained, EDA sequentially generates speaker-wise attractors on the basis of a sequence-to-sequence method using an LSTM encoder-decoder. The attractor generation continues until a stopping condition is satisfied; thus, the number of attractors can be flexible. Diarization results are then estimated as dot products of the attractors and embeddings. The embeddings from speaker overlaps result in larger dot product values with multiple attractors; thus, this method can deal with speaker overlaps. Because the maximum number of output speakers is still limited by the training set, we also propose an iterative inference method to remove this restriction. Further, we propose a method that aligns the estimated diarization results with the results of an external speech activity detector, which enables fair comparison against cascaded approaches. Extensive evaluations on simulated and real datasets show that EEND-EDA outperforms the conventional cascaded approach.
A comparative study of model approximation methods applied to economic MPC
Authors: Zhiyinan Huang, Qinyao Liu, Jinfeng Liu, Biao Huang
Date: 2021-06-21 00:00:00
Tasks: Model Predictive Control

Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing. It can lead to improving economic performance but at the same time increases the computational complexity significantly. Model approximation has been a standard approach for reducing computational complexity in process control. In this work, we perform a study on three types of representative model approximation methods applied to EMPC, including model reduction based on available first-principle models (e.g., proper orthogonal decomposition), system identification based on input-output data (e.g., subspace identification) that results in an explicitly expressed mathematical model, and neural networks based on input-output data. A representative algorithm from each model approximation method is considered. Two processes that are very different in dynamic nature and complexity were selected as benchmark processes for computational complexity and economic performance comparison, namely an alkylation process and a wastewater treatment plant (WWTP). The strengths and drawbacks of each method are summarized according to the simulation results, with future research direction regarding control oriented model approximation proposed at the end.
Design criteria of flexible capacitive pressure sensors using DIY-techniques and household materials
Authors: Rishabh Bhooshan Mishra
Date: 2021-06-21 00:00:00
Tasks: Sensitivity

The flexible capacitive pressure sensors are one of the most essential and famous devices with vast applications in automobile, aerospace, marine, healthcare, wearables, consumer, and portable electronics. The fabrication of pressure sensors in a cleanroom is expensive and time-consuming; however, the sensitivity, linearity, and other performance factors of those pressure sensors are exceptional. Moreover, sometimes we require sensors that are not expensive and can be fabricated rapidly where the other performance factors do not need to be highly remarkable. In this modern era, household materials and DIY (Do-it-yourself) techniques are quite helpful, highly utilized. They are recommended to fabricate low-cost sensors and healthcare devices for personalized medicine and low-cost consumer electronics. Different flexible capacitive pressure sensors are presented and experimentally characterized for acoustic and air-pressure monitoring in this thesis. The design criteria of a cantilever-based capacitive pressure sensor are discussed. The three different designs are analysed with aspect ratios of 1.5, 1.0, and 0.67. The sensor with an aspect ratio of 0.67 shows maximum sensitivity (mechanical and electrical), better response time, and the 1st and 2nd mode of resonant frequencies is comparatively less than the other two. The cantilever designs are susceptible to slight pressure; therefore, the diaphragm-based normal mode capacitive pressure sensor is introduced in the second chapter, which defines the design criteria of diaphragm shapes. The five different diaphragms analysed are circular, elliptical, pentagon, square, and rectangular shapes. The circular capacitive pressure sensor shows maximum sensitivity, however, maximum non-linear response.....
A Taxonomy to Unify Fault Tolerance Regimes for Automotive Systems: Defining Fail-Operational, Fail-Degraded, and Fail-Safe
Authors: Torben Stolte, Stefan Ackermann, Robert Graubohm, Inga Jatzkowski, Björn Klamann, Hermann Winner, Markus Maurer
Date: 2021-06-21 00:00:00

This paper presents a taxonomy that allows defining the fault tolerance regimes fail-operational, fail-degraded, and fail-safe in the context of automotive systems. Fault tolerance regimes such as these are widely used in recent publications related to automated driving, yet without definitions. This largely holds true for automotive safety standards, too. We show that fault tolerance regimes defined in scientific publications related to the automotive domain are partially ambiguous as well as taxonomically unrelated. The presented taxonomy is based on terminology stemming from ISO 26262 as well as from systems engineering. It uses four criteria to distinguish fault tolerance regimes. In addition to fail-operational, fail-degraded, and fail-safe, the core terminology consists of operational and fail-unsafe. These terms are supported by definitions of available performance, nominal performance, functionality, and a concise definition of the safe state. For verification, we show by means of two examples from the automotive domain that the taxonomy can be applied to hierarchical systems of different complexity.
Stability and performance in MPC using a finite-tail cost
Authors: Johannes Köhler, Frank Allgöwer
Date: 2021-01-12 00:00:00
Tasks: Model Predictive Control

In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs. We study the MPC formulation originally proposed by Magni et al. (2001) wherein the standard terminal penalty is replaced by a finite-horizon cost of some stabilizing control law. In order to analyse the closed loop, we leverage the more recent technical machinery developed for MPC without terminal ingredients. For a specified set of initial conditions, we obtain sufficient conditions for stability and a performance bound in dependence of the prediction horizon and the extended horizon used for the terminal penalty. The main practical benefit of the considered finite-tail cost MPC formulation is the simpler offline design in combination with typically significantly less restrictive bounds on the prediction horizon to ensure stability. We demonstrate the benefits of the considered MPC formulation using the classical example of a four tank system.
Realization of multi-input/multi-output switched linear systems from Markov parameters
Authors: Fethi Bencherki, Semiha Türkay, Hüseyin Akçay
Date: 2021-06-21 00:00:00

This paper presents a four-stage algorithm for the realization of multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov parameters. In the first stage, a linear time-varying (LTV) realization that is topologically equivalent to the true SLS is derived from the Markov parameters assuming that the submodels have a common MacMillan degree and a mild condition on their dwell times holds. In the second stage, zero sets of LTV Hankel matrices where the realized system has a linear time-invariant (LTI) pulse response matching that of the original SLS are exploited to extract the submodels, up to arbitrary similarity transformations, by a clustering algorithm using a statistics that is invariant to similarity transformations. Recovery is shown to be complete if the dwell times are sufficiently long and some mild identifiability conditions are met. In the third stage, the switching sequence is estimated by three schemes. The first scheme is based on forward/backward corrections and works on the short segments. The second scheme matches Markov parameter estimates to the true parameters for LTV systems and works on the medium-to-long segments. The third scheme also matches Markov parameters, but for LTI systems only and works on the very short segments. In the fourth stage, the submodels estimated in Stage 2 are brought to a common basis by applying a novel basis transformation method which is necessary before performing output predictions to given inputs. A numerical example illustrates the properties of the realization algorithm. A key role in this algorithm is played by time-dependent switching sequences that partition the state-space according to time, unlike many other works in the literature in which partitioning is state and/or input dependent.
Holographic Smart EM Skins for Advanced Beam Power Shaping in Next Generation Wireless Environments
Authors: Giacomo Oliveri, Paolo Rocca, Marco Salucci, Andrea Massa
Date: 2021-06-21 00:00:00

An innovative approach for the synthesis of inexpensive holographic smart electromagnetic (EM) skins with advanced beamforming features is proposed. The complex multiscale smart skin design is formulated within the Generalized Sheet Transition Condition (GSTC) framework as a combination of a mask-constrained isophoric inverse source problem and a micro-scale susceptibility dyadic optimization. The solution strategy integrates a local search procedure based on the iterative projection technique (IPT) and a System-by-Design (SbD)-based optimization loop for the identification of optimal metasurface descriptors matching the desired surface currents. The performance and the efficiency of the proposed approach are assessed in a set of representative test cases concerned with different smart skin apertures and target pattern masks.
Basis transform in switched linear system state-space models from input-output data
Authors: Fethi Bencherki, Semiha Türkay, Hüseyin Akçay
Date: 2021-06-21 00:00:00
Tasks: State Space Models

This paper addresses the problem of basis correction in the context of LSS identification from input-output data. It is often the case that identification algorithms for the LSSs from input-output data operate locally. The individually identified local submodel estimates reside in distinct state bases, which mandates performing a basis correction that facilitates their coherent patching for the ultimate goal of performing output predictions for arbitrary inputs and switching sequences. We formulate a persistence of excitation condition for the inputs and the switching sequences that guarantee the presented approach's success. These conditions are mild in nature, which proves the practicality of the devised algorithm. We supplement the theoretical findings with an elaborating numerical simulation example.
Covariance Matching based robust Adaptive Cubature Kalman Filter
Authors: Mundla Narasimhappa, Sesham Srinu
Date: 2021-06-20 00:00:00

This letter explores covariance matching-based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of conventional CKF. In the proposed algorithm, weights are adaptively adjusted and used for updating the measurement noise covariance matrices online. It can also enhance the adaptive capability of the ACKF. The simulation results are illustrated to evaluate the performance of the proposed algorithm.
Windowing and random weighting based cubature RTS smoothing for target tracking
Authors: Mundla Narasimhappa
Date: 2021-06-20 00:00:00

This paper presents windowing and random weighting (WRW) based adaptive cubature Rauch Tung Striebel (CRTS) smoother (WRWACRTS). The Unscented KF (WRWUKF) has already existed as an alternative to nonlinear smoothing solutions. In the proposed method, both windowing and random weighted estimation methods are combined together and used to estimate the noise statistics. Subsequently, the weights of each window are adjusting randomly, and update the process and measurement noise covariances matrices at each epoch. The developed WRWACRTS algorithm overcomes the limitation of the conventional CKS. The Lyapunov function-based approach is used to investigate the convergence proof of the WRWACRTS algorithm. A numerical example is shown to demonstrate the performance of the proposed algorithm.
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
Authors: Nan Li, Kaixiang Zhang, Zhaojian Li, Vaibhav Srivastava, Xiang Yin
Date: 2021-06-20 00:00:00
Tasks: Cloud Computing, Model Predictive Control

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks by explicitly considering model mismatches and errors due to request-response communication delays. We analyze stability-like properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-like conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials.
Optimal adaptive control of a knee joint exoskeleton for lower limb functional rehabilitation
Authors: Maria-Sara-Nour Sadoun, Fouad Yacef
Date: 2021-06-19 00:00:00

Lower limb exoskeleton robots hold great potential for rehabilitation, movement assistance, and strength augmentation. Design control to guarantee optimal needed assistance is still a challenge considering the pathological variances between patients. In this paper, we proposed an optimal adaptive control scheme based on Particle Swarm Optimization (PSO) Algorithm. The proposed controller is based on a well-known dynamic model of the knee joint exoskeleton, and the optimization algorithm is used to minimize a square error fitness function, which quantifies tracking performances. Control parameters are tuned respecting some nonlinear constraints for step response of the system and boundaries constraints. Numerical simulation results are presented to show the validity and the high performances of the proposed approach.