pwc-archive/papers-with-abstracts huggingface dataset:
Investigation of Deep Neural Network Acoustic Modelling Approaches for Low Resource Accented Mandarin Speech Recognition
Authors: Xurong Xie, Xiang Sui, Xunying Liu, Lan Wang
Date: 2022-01-24 00:00:00
Tasks: Acoustic Modelling, speech-recognition, Speech Recognition

The Mandarin Chinese language is known to be strongly influenced by a rich set of regional accents, while Mandarin speech with each accent is quite low resource. Hence, an important task in Mandarin speech recognition is to appropriately model the acoustic variabilities imposed by accents. In this paper, an investigation of implicit and explicit use of accent information on a range of deep neural network (DNN) based acoustic modelling techniques is conducted. Meanwhile, approaches of multi-accent modelling including multi-style training, multi-accent decision tree state tying, DNN tandem and multi-level adaptive network (MLAN) tandem hidden Markov model (HMM) modelling are combined and compared in this paper. On a low resource accented Mandarin speech recognition task consisting of four regional accents, an improved MLAN tandem HMM systems explicitly leveraging the accent information was proposed and significantly outperformed the baseline accent independent DNN tandem systems by 0.8%-1.5% absolute (6%-9% relative) in character error rate after sequence level discriminative training and adaptation.
Polyphone disambiguation and accent prediction using pre-trained language models in Japanese TTS front-end
Authors: Rem Hida, Masaki Hamada, Chie Kamada, Emiru Tsunoo, Toshiyuki Sekiya, Toshiyuki Kumakura
Date: 2022-01-24 00:00:00
Tasks: Morphological Analysis, Polyphone disambiguation, Sentence, text-to-speech, Text to Speech

Although end-to-end text-to-speech (TTS) models can generate natural speech, challenges still remain when it comes to estimating sentence-level phonetic and prosodic information from raw text in Japanese TTS systems. In this paper, we propose a method for polyphone disambiguation (PD) and accent prediction (AP). The proposed method incorporates explicit features extracted from morphological analysis and implicit features extracted from pre-trained language models (PLMs). We use BERT and Flair embeddings as implicit features and examine how to combine them with explicit features. Our objective evaluation results showed that the proposed method improved the accuracy by 5.7 points in PD and 6.0 points in AP. Moreover, the perceptual listening test results confirmed that a TTS system employing our proposed model as a front-end achieved a mean opinion score close to that of synthesized speech with ground-truth pronunciation and accent in terms of naturalness.
Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition
Authors: Xurong Xie, Rukiye Ruzi, Xunying Liu, Lan Wang
Date: 2022-01-24 00:00:00
Tasks: speech-recognition, Speech Recognition

Dysarthric speech recognition is a challenging task due to acoustic variability and limited amount of available data. Diverse conditions of dysarthric speakers account for the acoustic variability, which make the variability difficult to be modeled precisely. This paper presents a variational auto-encoder based variability encoder (VAEVE) to explicitly encode such variability for dysarthric speech. The VAEVE makes use of both phoneme information and low-dimensional latent variable to reconstruct the input acoustic features, thereby the latent variable is forced to encode the phoneme-independent variability. Stochastic gradient variational Bayes algorithm is applied to model the distribution for generating variability encodings, which are further used as auxiliary features for DNN acoustic modeling. Experiment results conducted on the UASpeech corpus show that the VAEVE based variability encodings have complementary effect to the learning hidden unit contributions (LHUC) speaker adaptation. The systems using variability encodings consistently outperform the comparable baseline systems without using them, and" obtain absolute word error rate (WER) reduction by up to 2.2% on dysarthric speech with "Very lowintelligibility level, and up to 2% on the "Mixed" type of dysarthric speech with diverse or uncertain conditions.
Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
Authors: Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang, Li-Rong Dai
Date: 2022-01-22 00:00:00

In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals to train the network, respectively, we can build individual models for three tasks, whose parameters are averaged to obtain an average model, which is then used as the initialization for the BiLSTM model training of each task. This initialization method can significantly improve the performance on the three tasks, which surpasses the official baseline results. Besides, we also utilize a public pre-trained model wav2vec2.0 and pre-train it using the official DiCOVA datasets. This wav2vec2.0 model is utilized to extract high-level features of the sound as the model input to replace conventional mel-frequency cepstral coefficients (MFCC) features. Experimental results reveal that using high-level features together with MFCC features can improve the performance. To further improve the performance, we also deploy some preprocessing techniques like silent segment removal, amplitude normalization and time-frequency mask. The proposed detection model is evaluated on the DiCOVA dataset and results show that our method achieves an area under curve (AUC) score of 88.44% on blind test in the fusion track.
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition
Authors: Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang, Ming-Hui Wu, Xin Fang, Li-Rong Dai
Date: 2022-01-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Representation Learning, speech-recognition, Speech Recognition, Speech Representation Learning

Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.0 has a good robustness against the domain shift, while the noise robustness is still unclear. In this work, we therefore first analyze the noise robustness of wav2vec2.0 via experiments. We observe that wav2vec2.0 pre-trained on noisy data can obtain good representations and thus improve the ASR performance on the noisy test set, which however brings a performance degradation on the clean test set. To avoid this issue, in this work we propose an enhanced wav2vec2.0 model. Specifically, the noisy speech and the corresponding clean version are fed into the same feature encoder, where the clean speech provides training targets for the model. Experimental results reveal that the proposed method can not only improve the ASR performance on the noisy test set which surpasses the original wav2vec2.0, but also ensure a tiny performance decrease on the clean test set. In addition, the effectiveness of the proposed method is demonstrated under different types of noise conditions.
Pseudospectral continuation for aeroelastic stability analysis
Authors: Arion Pons
Date: 2022-01-24 00:00:00

This technical note is concerned with aeroelastic flutter problems: the analysis of aeroelastic systems undergoing airspeed-dependent dynamic instability. Existing continuation methods for parametric stability analysis are based on marching along an airspeed parameter until the flutter point is found - an approach which may waste computational effort on low-airspeed system behavior, before a flutter point is located and characterized. Here, we describe a pseudospectral continuation approach which instead marches outwards from the system's flutter points, from points of instability to points of increasing damping, allowing efficient characterization of the subcritical and supercritical behavior of the system. This approach ties together aeroelastic stability analysis and abstract linear algebra, and provides efficient methods for computing practical aeroelastic stability properties - for instance, flight envelopes based on maximum modal damping, and the location of borderline-stable zones.
Non-Linear Dynamic Inversion with Actuator Dynamics: an Incremental Control Perspective
Authors: Rasmus Steffensen, Agnes Steinert, Ewoud Jan Jacob Smeur
Date: 2022-01-24 00:00:00

In this paper, we derive a sensor based Nonlinear Dynamic Inversion (NDI) control law for a nonlinear system with first-order linear actuators, and compare it to Incremental Nonlinear Dynamic Inversion (INDI), which has gained popularity in recent years. It is shown that for first-order actuator dynamics, INDI approximates the corresponding NDI control law arbitrarily well under the condition of sufficiently fast actuators. If the actuator bandwidth is low compared to changes in the states, the derived NDI control law has the following advantages compared to INDI: 1) compensation of state derivative terms 2) well defined error dynamics and 3) exact tracking of a reference model, independent of error controller gains in nominal conditions. The comparison of the INDI control law with the well established control design method NDI adds to the understanding of incremental control. It is additionally shown how to quantify the deficiency of the INDI control law with respect to the exact NDI law for actuators with finite bandwidth. The results are confirmed through simulation results of the rolling motion of a fixed wing aircraft.
A Critical Review of Baseband Architectures for CubeSats Communication Systems
Authors: Amr Zeedan, Tamer Khattab
Date: 2022-01-24 00:00:00

Small satellite communications recently entered a period of massive interest driven by the uprising space applications. CubeSats are particularly attractive due to their low development costs which makes them very promising in playing a central role in the global wireless communication sector with numerous applications. Moreover, constellations of CubeSats in low-earth orbits can meet the increasing demands of global-coverage flexible low-cost high-speed connectivity. However, this requires innovative solutions to overcome the significant challenges that face high-data-rate low-power space communications. This paper provides a comprehensive and critical review of the design and architecture of recent CubeSat communication systems with a particular focus on their baseband architectures. The literature is surveyed in detail to identify all baseband design, testing, and demonstration stages as well as accurately describe the systems architecture and communication protocols. The reliability, performance, data rate, and power consumption of the reviewed systems are critically evaluated to understand the limitations of current CubeSat systems and identify directions of future developments. It is concluded that CubeSat communication systems still face many challenges, namely the development of energy-efficient high-speed modems that satisfy CubeSats requirements. Nevertheless, there are several promising directions for improvements such as the use of improved coding algorithms, use of Field Programmable Gate Arrays, multiple access techniques, beamforming, advanced antennas, and transition to higher frequency bands. By providing a concrete summary of current CubeSat communication systems and by critically evaluating their features, limitations, and offering insights about potential improvements, the review should aid CubeSat developers to develop more efficient and high data rate systems.
LCOE-based Pricing for DLT-enabled Local Energy Trading Platforms
Authors: Marthe Fogstad Dynge, Ugur Halden, Gro Klæboe, Umit Cali
Date: 2022-01-24 00:00:00
Tasks: energy trading

Support schemes like the Feed-in-Tariff (FiT) have for many years been an important driver for the deployment of distributed energy resources, and the transition from consumerism to prosumerism. This democratization and decarbonization of the energy system has led to both challenges and opportunities for the system operators, paving the way for emerging concepts like local energy markets. The FiT approach has often been assumed as the lower economic bound for a prosumer's willingness to participate in such markets but is now being phased out in several countries. In this paper, a new pricing mechanism based on the Levelized Cost of Electricity is proposed, with the intention of securing profitability for the prosumers, as well as creating a transparent and fair price for all market participants. The mechanism is designed to function on a Distributed Ledger Technology-based platform and is further set up from a holistic perspective, defining the market framework as interactions in a Cyber-Physical-Social-System. Schemes based on both fixed and variable contracts with the wholesale supplier are analyzed and compared with both the conventional FiT and to its proposed replacement options. The results show a cost reduction for the consumers and a slight loss in revenue for the prosumers compared to the FiT scheme. Comparing it to the actual suggested replacements to the FiT, however, it is clear that the pricing mechanism proposed in this study provides a substantial increase of benefits for both prosumers and consumers.
Mars Entry Trajectory Planning with Range Discretization and Successive Convexification
Authors: Xu Liu, Shuang Li, Ming Xin
Date: 2022-01-24 00:00:00
Tasks: Numerical Integration, Trajectory Planning

This paper develops a sequential convex programming approach for Mars entry trajectory planning by range discretization. To improve the accuracy of numerical integration, the range of entry trajectory is selected as the independent variable rather than time or energy. A dilation factor is employed to normalize the entry dynamics and integration interval of the performance index so that the difficult free-final-time programming problem can be converted to a fixed-final-range optimization problem. The bank angle rate with respect to the range is introduced as the new control input in order to decouple the control from the state and facilitate convexification of constraints on the bank angle and its rate. The nonlinear bank angle rate constraint is further relaxed into a linear one via inequality relaxation. Moreover, the nonconvex minimum-time performance index is convexified by regarding flight time as a state variable. Then, the Mars entry trajectory planning problem can be formulated into the framework of convex programming after linearization. By range discretization and successive convexification, the reformulated Mars entry trajectory planning problem is transcribed into a series of convex optimization sub-problems that can be sequentially solved using the convex programming algorithm. The virtual control and adaptive trust-region techniques are employed to improve the accuracy, robustness, and computation efficiency of the algorithm. Numerical simulations with comparative studies are presented to demonstrate the convergence performance and efficiency of the proposed algorithm.
Novel Nussbaum-Type Function based Safe Adaptive Distributed Consensus Control with Arbitrary Unknown Control Direction
Authors: Dan Qiao, Zhaoxia Peng, Guoguang Wen, TingWen Huang
Date: 2022-01-24 00:00:00

Existing Nussbaum function based methods on the consensus of multi-agent systems require (partial) identical unknown control directions of all agents and cause dangerous dramatic control shocks. This paper develops a novel saturated Nussbaum function to relax such limitations and proposes a Nussbaum function based control scheme for the consensus problem of multi-agent systems with arbitrary non-identical unknown control directions and safe control progress. First, a novel type of the Nussbaum function with different frequencies is proposed in the form of saturated time-elongation functions, which provides a more smooth and safer transient performance of the control progress. Furthermore, the novel Nussbaum function is employed to design distributed adaptive control algorithms for linearly parameterized multi-agent systems to achieve average consensus cooperatively without dramatic control shocks. Then, under the undirected connected communication topology, all the signals of the closed-loop systems are proved to be bounded and asymptotically convergent. Finally, two comparative numerical simulation examples are carried out to verify the effectiveness and the superiority of the proposed approach with smaller control shock amplitudes than traditional Nussbaum methods.
Prescribed Performance Adaptive Fixed-Time Attitude Tracking Control of a 3-DOF Helicopter with Small Overshoot
Authors: Xidong Wang
Date: 2022-01-23 00:00:00

In this article, a novel prescribed performance adaptive fixed-time backstepping control strategy is investigated for the attitude tracking of a 3-DOF helicopter. First, a new unified barrier function (UBF) is designed to convert the prescribed performance constrained system into an unconstrained one. Then, a fixed-time (FxT) backstepping control framework is established to achieve the attitude tracking. By virtual of a newly proposed inequality, a non-singular virtual control law is constructed. In addition, a FxT differentiator with a compensation mechanism is employed to overcome the matter of "explosion of complexity". Moreover, a modified adaptive law is developed to approximate the upper bound of the disturbances. To obtain a less conservative and more accurate approximation of the settling time, an improved FxT stability theorem is proposed. Based on this theorem, it is proved that all signals of the system are FxT bounded, and the tracking error converges to a preset domain with small overshoot in a user-defined time. Finally, the feasibility and effectiveness of the presented control strategy are confirmed by numerical simulations.
Excitation allocation for generic identifiability of linear dynamic networks with fixed modules
Authors: H. J. Dreef, S. Shi, X. Cheng, M. C. F. Donkers, P. M. J. Van den Hof
Date: 2022-01-22 00:00:00

Identifiability of linear dynamic networks requires the presence of a sufficient number of external excitation signals. The problem of allocating a minimal number of external signals for guaranteeing generic network identifiability has been recently addressed in the literature. Here we will extend that work by explicitly incorporating the situation that some network modules are known, and thus are fixed in the parametrized model set. The graphical approach introduced earlier is extended to this situation, showing that the presence of fixed modules reduces the required number of external signals. An algorithm is presented that allocates the external signals in a systematic fashion.
Slotted ALOHA and CSMA Protocols for FMCW Radar Networks
Authors: Haritha K, Vineeth Bala Sukumaran, Chandramani Singh
Date: 2022-01-22 00:00:00

We study medium access in FMCW radar networks. We assume that all the radars use same parameters, e.g., chirp duration, chirp slope, cutoff frequency, number of chirps per packet, etc, and propose and analyze slotted ALOHA and CSMA protocols to mitigate narrowband interference. We define a notion of throughput to quantify the performance of the proposed protocols. In the case of ALOHA, we analyze interference probability and throughput as functions of the system parameters. We observe that interference probability and throughput may behave differently than in wireless communication networks. For instance, if the number of chirps per packet is larger than one, the interference probabilities may be smaller for higher transmission rates. We define a medium sensing procedure, referred to as clear channel assessment (CCA), as a part of the proposed CSMA, and also define CCA success and failure events. In CSMA, the radars transmit only after a successful CCA. We study, CCA success probability, interference probability and throughput as functions of the system parameters. We observe that, unlike wireless communication networks, using the highest possible attempt rates may maximize throughput in few network scenarios. We perform an extensive simulation to verify our analytical results and to compare slotted ALOHA and CSMA. We observe that CSMA outperforms ALOHA in all realistic scenarios.
Nonlinear $H_{\infty}$ Filtering on the Special Orthogonal Group $SO(3)$ using Vector Directions
Authors: Farooq Aslam, Muhammad Farooq Haydar
Date: 2022-01-22 00:00:00

The problem of $H_{\infty}$ filtering for attitude estimation using rotation matrices and vector measurements is studied. Starting from a storage function on the Special Orthogonal Group $SO(3)$, a dissipation inequality is considered, and a deterministic nonlinear $H_{\infty}$ filter is derived which respects a given upper bound $\gamma$ on the energy gain from exogenous disturbances and initial estimation errors to a generalized estimation error. The results are valid for all estimation errors which correspond to an angular error of less than $\pi/2$ radians in terms of the axis-angle representation. The approach builds on earlier results on attitude estimation, in particular nonlinear $H_{\infty}$ filtering using quaternions, and proposes a novel filter developed directly on $SO(3)$. The proposed filter employs the same innovation term as the Multiplicative Extended Kalman Filter (MEKF), as well as a matrix gain updated in accordance with a Riccati-type gain update equation. However, in contrast to the MEKF, the proposed filter has an additional tuning gain, $\gamma$, which enables it to be more aggressive during transients. The filter is simulated for different conditions, and the results are compared with those obtained using the continuous-time quaternion MEKF and the Geometric Approximate Minimum Energy (GAME) filter. Simulations indicate competitive performance. In particular, the GAME filter has the best transient performance, followed by the proposed $H_{\infty}$ filter and the quaternion MEKF. All three filters have similar steady-state performance. Therefore, the proposed filter can be seen as a MEKF variant which achieves better transient performance without significant degradation in steady-state noise rejection.