pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets
Authors: Islam Ali, Hong Zhang
Date: 2022-09-13 00:00:00
Tasks: Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly available datasets which are increasing in number and the level of difficulty. Each dataset provides a certain level of dynamic range coverage that is a key aspect of measuring the robustness and resilience of SLAM. In this paper, we provide a systematic analysis of the dynamic range coverage of datasets based on a number of characterization metrics, and our analysis shows a huge level of redundancy within and between datasets. Subsequently, we propose a dynamic programming (DP) algorithm for eliminating the redundancy in the evaluation process of SLAM by selecting a subset of sequences that matches a single or multiple dynamic range coverage objectives. It is shown that, with the help of dataset characterization and DP selection algorithm, a reduction in the evaluation effort can be achieved while maintaining the same level of coverage. We also study how the evaluation process of a real-world SLAM system can be optimized utilizing the method proposed.
Multiple View Performers for Shape Completion
Authors: David Watkins-Valls, Peter Allen, Krzysztof Choromanski, Jacob Varley, Nicholas Waytowich
Date: 2022-09-13 00:00:00

We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views. MVP accomplishes this task by using linear-attention Transformers called Performers. Our model allows the current observation of the scene to attend to the previous ones for more accurate infilling. The history of past observations is compressed via the compact associative memory approximating modern continuous Hopfield memory, but crucially of size independent from the history length. We compare our model with several baselines for shape completion over time, demonstrating the generalization gains that MVP provides. To the best of our knowledge, MVP is the first multiple view voxel reconstruction method that does not require registration of multiple depth views and the first causal Transformer based model for 3D shape completion.
ParaTTS: Learning Linguistic and Prosodic Cross-sentence Information in Paragraph-based TTS
Authors: Liumeng Xue, Frank K. Soong, Shaofei Zhang, Lei Xie
Date: 2022-09-14 00:00:00
Tasks: Position, Sentence

Recent advancements in neural end-to-end TTS models have shown high-quality, natural synthesized speech in a conventional sentence-based TTS. However, it is still challenging to reproduce similar high quality when a whole paragraph is considered in TTS, where a large amount of contextual information needs to be considered in building a paragraph-based TTS model. To alleviate the difficulty in training, we propose to model linguistic and prosodic information by considering cross-sentence, embedded structure in training. Three sub-modules, including linguistics-aware, prosody-aware and sentence-position networks, are trained together with a modified Tacotron2. Specifically, to learn the information embedded in a paragraph and the relations among the corresponding component sentences, we utilize linguistics-aware and prosody-aware networks. The information in a paragraph is captured by encoders and the inter-sentence information in a paragraph is learned with multi-head attention mechanisms. The relative sentence position in a paragraph is explicitly exploited by a sentence-position network. Trained on a storytelling audio-book corpus (4.08 hours), recorded by a female Mandarin Chinese speaker, the proposed TTS model demonstrates that it can produce rather natural and good-quality speech paragraph-wise. The cross-sentence contextual information, such as break and prosodic variations between consecutive sentences, can be better predicted and rendered than the sentence-based model. Tested on paragraph texts, of which the lengths are similar to, longer than, or much longer than the typical paragraph length of the training data, the TTS speech produced by the new model is consistently preferred over the sentence-based model in subjective tests and confirmed in objective measures.
A Universally-Deployable ASR Frontend for Joint Acoustic Echo Cancellation, Speech Enhancement, and Voice Separation
Authors: Tom O'Malley, Arun Narayanan, Quan Wang
Date: 2022-09-14 00:00:00
Tasks: Acoustic echo cancellation, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Recent work has shown that it is possible to train a single model to perform joint acoustic echo cancellation (AEC), speech enhancement, and voice separation, thereby serving as a unified frontend for robust automatic speech recognition (ASR). The joint model uses contextual information, such as a reference of the playback audio, noise context, and speaker embedding. In this work, we propose a number of novel improvements to such a model. First, we improve the architecture of the Cross-Attention Conformer that is used to ingest noise context into the model. Second, we generalize the model to be able to handle varying lengths of noise context. Third, we propose Signal Dropout, a novel strategy that models missing contextual information. In the absence of one or more signals, the proposed model performs nearly as well as task-specific models trained without these signals; and when such signals are present, our system compares well against systems that require all context signals. Over the baseline, the final model retains a relative word error rate reduction of 25.0% on background speech when speaker embedding is absent, and 61.2% on AEC when device playback is absent.
Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset
Authors: Michael Chinen, Jan Skoglund, Chandan K A Reddy, Alessandro Ragano, Andrew Hines
Date: 2022-09-14 00:00:00
Tasks: text-to-speech, Text to Speech, Voice Conversion

Non-reference speech quality models are important for a growing number of applications. The VoiceMOS 2022 challenge provided a dataset of synthetic voice conversion and text-to-speech samples with subjective labels. This study looks at the amount of variance that can be explained in subjective ratings of speech quality from metadata and the distribution imbalances of the dataset. Speech quality models were constructed using wav2vec 2.0 with additional metadata features that included rater groups and system identifiers and obtained competitive metrics including a Spearman rank correlation coefficient (SRCC) of 0.934 and MSE of 0.088 at the system-level, and 0.877 and 0.198 at the utterance-level. Using data and metadata that the test restricted or blinded further improved the metrics. A metadata analysis showed that the system-level metrics do not represent the model's system-level prediction as a result of the wide variation in the number of utterances used for each system on the validation and test datasets. We conclude that, in general, conditions should have enough utterances in the test set to bound the sample mean error, and be relatively balanced in utterance count between systems, otherwise the utterance-level metrics may be more reliable and interpretable.
Deep Speech Synthesis from Articulatory Representations
Authors: Peter Wu, Shinji Watanabe, Louis Goldstein, Alan W Black, Gopala K. Anumanchipalli
Date: 2022-09-13 00:00:00
Tasks: Speech Synthesis

In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract. This task provides a promising direction for speech synthesis research, as the articulatory space is compact, smooth, and interpretable. Current works have highlighted the potential for deep learning models to perform articulatory synthesis. However, it remains unclear whether these models can achieve the efficiency and fidelity of the human speech production system. To help bridge this gap, we propose a time-domain articulatory synthesis methodology and demonstrate its efficacy with both electromagnetic articulography (EMA) and synthetic articulatory feature inputs. Our model is computationally efficient and achieves a transcription word error rate (WER) of 18.5% for the EMA-to-speech task, yielding an improvement of 11.6% compared to prior work. Through interpolation experiments, we also highlight the generalizability and interpretability of our approach.
Automated detection of pronunciation errors in non-native English speech employing deep learning
Authors: Daniel Korzekwa
Date: 2022-09-13 00:00:00
Tasks: Speech Synthesis

Despite significant advances in recent years, the existing Computer-Assisted Pronunciation Training (CAPT) methods detect pronunciation errors with a relatively low accuracy (precision of 60% at 40%-80% recall). This Ph.D. work proposes novel deep learning methods for detecting pronunciation errors in non-native (L2) English speech, outperforming the state-of-the-art method in AUC metric (Area under the Curve) by 41%, i.e., from 0.528 to 0.749. One of the problems with existing CAPT methods is the low availability of annotated mispronounced speech needed for reliable training of pronunciation error detection models. Therefore, the detection of pronunciation errors is reformulated to the task of generating synthetic mispronounced speech. Intuitively, if we could mimic mispronounced speech and produce any amount of training data, detecting pronunciation errors would be more effective. Furthermore, to eliminate the need to align canonical and recognized phonemes, a novel end-to-end multi-task technique to directly detect pronunciation errors was proposed. The pronunciation error detection models have been used at Amazon to automatically detect pronunciation errors in synthetic speech to accelerate the research into new speech synthesis methods. It was demonstrated that the proposed deep learning methods are applicable in the tasks of detecting and reconstructing dysarthric speech.
Automatic Comment Generation via Multi-Pass Deliberation
Authors: Fangwen Mu, Xiao Chen, Lin Shi, Song Wang, Qing Wang
Date: 2022-09-14 00:00:00
Tasks: Articles, Comment Generation

Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful.
Vibration Compensation of Delta 3D Printer with Position-varying Dynamics using Filtered B-Splines
Authors: Nosakhare Edoimioya, Cheng-Hao Chou, Chinedum E. Okwudire
Date: 2022-09-14 00:00:00
Tasks: Position

The delta robot is becoming a popular choice for the mechanical design of fused filament fabrication 3D printers because it can reach higher speeds than traditional serial-axis designs. Like serial 3D printers, delta printers suffer from undesirable vibration at high speeds which degrades the quality of fabricated parts. This undesirable vibration has been suppressed in serial printers using linear model-inversion feedforward control methods like the filtered B-splines (FBS) approach. However, techniques like the FBS approach are computationally challenging to implement on delta 3D printers because of their coupled, position-dependent dynamics. In this paper, we propose a methodology to address the computational bottlenecks by (1) parameterizing the position-dependent portions of the dynamics offline to enable efficient online model generation, (2) computing real-time models at sampled points (instead of every point) along the given trajectory, and (3) employing QR factorization to reduce the number of floating-point arithmetic operations associated with matrix inversion. In simulations, we report a computation time reduction of up to 23x using the proposed method, while maintaining high accuracy, when compared to a controller using the computationally expensive exact LPV model. In experiments, we demonstrate significant quality improvements on parts printed at various positions on the delta 3D printer using our proposed controller compared to a baseline alternative, which uses an LTI model from one position. Through acceleration measurements during printing, we also show that the print quality boost of the proposed controller is due to vibration reductions of more than 20\% when compared to the baseline controller.
Falsification of Cyber-Physical Systems using Bayesian Optimization
Authors: Zahra Ramezani, Kenan Šehić, Luigi Nardi, Knut Åkesson
Date: 2022-09-14 00:00:00
Tasks: Bayesian Optimization

Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system's specifications are met. Simulation-based falsification is a practical testing technique for increasing confidence in a CPS's correctness, as it only requires that the system be simulated. Reducing the number of computationally intensive simulations needed for falsification is a key concern. In this study, we investigate Bayesian optimization (BO), a sample-efficient approach that learns a surrogate model to capture the relationship between input signal parameterization and specification evaluation. We propose two enhancements to the basic BO for improving falsification: (1) leveraging local surrogate models, and (2) utilizing the user's prior knowledge. Additionally, we address the formulation of acquisition functions for falsification by proposing and evaluating various alternatives. Our benchmark evaluation demonstrates significant improvements when using local surrogate models in BO for falsifying challenging benchmark examples. Incorporating prior knowledge is found to be especially beneficial when the simulation budget is constrained. For some benchmark problems, the choice of acquisition function noticeably impacts the number of simulations required for successful falsification.
Virtual Inertia Scheduling for Power Systems with High Penetration of Inverter-based Resources
Authors: Buxin She, Fangxing Li, Hantao Cui, Jinnng Wang, Qiwei Zhang, Rui Bo
Date: 2022-09-14 00:00:00
Tasks: Management, Scheduling

This paper proposes a new concept called virtual inertia scheduling (VIS) to efficiently handle the high penetration of inverter-based resources (IBRs). VIS is an inertia management framework that targets security-constrained and economy-oriented inertia scheduling and generation dispatch of power systems with a large scale of renewable generations. Specifically, it schedules the proper power setting points and reserved capacities of both synchronous generators and IBRs, as well as the control modes and control parameters of IBRs to provide secure and cost-effective inertia support. First, a uniform system model is employed to quantify the frequency dynamics of the IBRs-penetrated power system after disturbances. Based on the model, the s-domain and time-domain analytical responses of IBRs with inertia support capability are derived. Then, VIS-based real-time economic dispatch (VIS-RTED) is formulated to minimize generation and reserve costs, with a full consideration of dynamic frequency constraints and derived inertia support reserve constraints. The virtual inertia and damping of IBRs are formulated as decision variables. To address the non-linearity of dynamic constraints, deep learning-assisted linearization is employed to solve the optimization problem. Finally, the proposed VIS-RTED is demonstrated on a modified IEEE 39-bus system. A full-order time-domain simulation is performed to verify the scheduling results.
Efficient Beam Search for Initial Access Using Collaborative Filtering
Authors: George Yammine, Georgios Kontes, Norbert Franke, Axel Plinge, Christopher Mutschler
Date: 2022-09-14 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest power is radiated towards (and received by) the user equipment (UE). While there are methods that improve upon an exhaustive search for optimal beams by some form of hierarchical search, they can be prone to return only locally optimal solutions with small beam gains. Other approaches address this problem by exploiting contextual information, e.g., the position of the UE or information from neighboring base stations (BS), but the burden of computing and communicating this additional information can be high. Methods based on machine learning so far suffer from the accompanying training, performance monitoring and deployment complexity that hinders their application at scale. This paper proposes a novel method for solving the initial beam-discovery problem. It is scalable, and easy to tune and to implement. Our algorithm is based on a recommender system that associates groups (i.e., UEs) and preferences (i.e., beams from a codebook) based on a training data set. Whenever a new UE needs to be served our algorithm returns the best beams in this user cluster. Our simulation results demonstrate the efficiency and robustness of our approach, not only in single BS setups but also in setups that require a coordination among several BSs. Our method consistently outperforms standard baseline algorithms in the given task.
Value of Bidirectional V2G Smart Charging Responsive Services: Insights from a Simple CA Model
Authors: Pedro M. S. Carvalho, Luis A. F. M. Ferreira
Date: 2022-09-14 00:00:00

In this paper, particle-hopping cellular automaton (CA) models of elastic demand are used to investigate the value added to plug-in electric vehicles (PEV) aggregators by adopting vehicle-to-grid (V2G) responsive services. CA models used earlier to study load-sifting responses are modified to capture discharge/ recharge capabilities of V2G. Results on ramping responses from CA are then analysed to discuss the small contribution to system controllability added by V2G responsive services.
On modal observers for beyond rigid body $H_\infty$ control in high-precision mechatronics
Authors: Yorick Broens, Hans Butler, Roland Tóth
Date: 2022-09-14 00:00:00
Tasks: Position

The ever increasing need for performance results in increasingly rigorous demands on throughput and positioning accuracy of high-precision motion systems, which often suffer from position dependent effects that originate from relative actuation and sensing of the moving-body. Due to the highly stiff mechanical design, such systems are typically controlled using rigid body control design approaches. Nonetheless, the presence of position dependent flexible dynamics severely limits attainable position tracking performance. This paper presents two extensions of the conventional rigid body control framework towards active control of position dependent flexible dynamics. Additionally, a novel control design approach is presented, which allows for shaping of the full closed-loop system by means of structured $H_\infty$ co-design. The effectiveness of the approach is validated through simulation using a high-fidelity model of a state-of-the-art moving-magnet planar actuator.
Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression
Authors: Max van Meer, Gert Witvoet, Tom Oomen
Date: 2022-09-14 00:00:00
Tasks: regression

Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.
Electric Vehicle Battery Sharing Game for Mobile Energy Storage Provision in Power Networks
Authors: Utkarsha Agwan, Junjie Qin, Kameshwar Poolla, Pravin Varaiya
Date: 2022-09-14 00:00:00

Electric vehicles (EVs) equipped with a bidirectional charger can provide valuable grid services as mobile energy storage. However, proper financial incentives need to be in place to enlist EV drivers to provide services to the grid. In this paper, we consider two types of EV drivers who may be willing to provide mobile storage service using their EVs: commuters taking a fixed route, and on-demand EV drivers who receive incentives from a transportation network company (TNC) and are willing to take any route. We model the behavior of each type of driver using game theoretic methods, and characterize the Nash equilibrium (NE) of an EV battery sharing game where each EV driver withdraws power from the grid to charge its EV battery at the origin of a route, travels from the origin to the destination, and then discharges power back to the grid at the destination of the route. The driver earns a payoff that depends on the participation of other drivers and power network conditions. We characterize the NE in three situations: when there are only commuters, when there are only on-demand TNC drivers, and when the two groups of drivers co-exist. In particular, we show that the equilibrium outcome supports the social welfare in each of these three cases.
Design space exploration of a poultry fillet processing system using discrete-event simulation
Authors: N. Paape, J. A. W. M. van Eekelen, M. A. Reniers
Date: 2022-09-14 00:00:00

Developments in the poultry processing industry, such as how livestock is raised and how consumers buy meat, make it increasingly difficult to design poultry processing systems that meet evolving standards. More and more iterations of (re)design are required to optimize the product flow in these systems. This paper presents a method for design space exploration of production systems using discrete-event simulation. This method automates most steps of design space exploration: iterating on the design, model construction, performing simulation experiments, and interpreting the simulation results. This greatly reduces the time and effort required to iterate through different designs. A case study is presented which shows that this method can be effective for design space exploration of poultry processing systems.