pwc-archive/papers-with-abstracts huggingface dataset:
Impact of Different Desired Velocity Profiles and Controller Gains on Convoy Driveability of Cooperative Adaptive Cruise Control Operated Platoons
Authors: Santhosh Tamilarasan, Levent Guvenc
Date: 2023-06-03 00:00:00
Tasks: Autonomous Vehicles

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.
Non-bilaterians as Model Systems for Tissue Mechanics
Authors: Setareh Gooshvar, Gopika Madhu, Melissa Ruszczyk, Vivek N. Prakash
Date: 2023-06-02 00:00:00
Tasks: model

In animals, epithelial tissues are barriers against the external environment, providing protection against biological, chemical, and physical damage. Depending on the animal's physiology and behavior, these tissues encounter different types of mechanical forces and need to provide a suitable adaptive response to ensure success. Therefore, understanding tissue mechanics in different contexts is an important research area. Here, we review recent tissue mechanics discoveries in a few early-divergent non-bilaterian animals -- Trichoplax adhaerens, Hydra vulgaris, and Aurelia aurita. We highlight each animal's simple body plan and biology, and unique, rapid tissue remodeling phenomena that play a crucial role in its physiology. We also discuss the emergent large-scale mechanics that arise from small-scale phenomena. Finally, we emphasize the enormous potential of these non-bilaterian animals to be model systems for further investigation in tissue mechanics.
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
Authors: Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
Date: 2023-05-29 00:00:00

Simulations of turbulent flows in 3D are one of the most expensive simulations in computational fluid dynamics (CFD). Many works have been written on surrogate models to replace numerical solvers for fluid flows with faster, learned, autoregressive models. However, the intricacies of turbulence in three dimensions necessitate training these models with very small time steps, while generating realistic flow states requires either long roll-outs with many steps and significant error accumulation or starting from a known, realistic flow state - something we aimed to avoid in the first place. Instead, we propose to approach turbulent flow simulation as a generative task directly learning the manifold of all possible turbulent flow states without relying on any initial flow state. For our experiments, we introduce a challenging 3D turbulence dataset of high-resolution flows and detailed vortex structures caused by various objects and derive two novel sample evaluation metrics for turbulent flows. On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state.
Inference for Local Projections
Authors: Atsushi Inoue, Òscar Jordà, Guido M. Kuersteiner
Date: 2023-06-05 00:00:00

Inference for impulse responses estimated with local projections presents interesting challenges and opportunities. Analysts typically want to assess the precision of individual estimates, explore the dynamic evolution of the response over particular regions, and generally determine whether the impulse generates a response that is any different from the null of no effect. Each of these goals requires a different approach to inference. In this article, we provide an overview of results that have appeared in the literature in the past 20 years along with some new procedures that we introduce here.
Improving the accuracy of bubble date estimators under time-varying volatility
Authors: Eiji Kurozumi, Anton Skrobotov
Date: 2023-06-05 00:00:00

In this study, we consider a four-regime bubble model under the assumption of time-varying volatility and propose the algorithm of estimating the break dates with volatility correction: First, we estimate the emerging date of the explosive bubble, its collapsing date, and the recovering date to the normal market under assumption of homoskedasticity; second, we collect the residuals and then employ the WLS-based estimation of the bubble dates. We demonstrate by Monte Carlo simulations that the accuracy of the break dates estimators improve significantly by this two-step procedure in some cases compared to those based on the OLS method.
Synthetic Regressing Control Method
Authors: Rong J. B. Zhu
Date: 2023-06-05 00:00:00

Estimating weights in the synthetic control method, typically resulting in sparse weights where only a few control units have non-zero weights, involves an optimization procedure that simultaneously selects and aligns control units to closely match the treated unit. However, this simultaneous selection and alignment of control units may lead to a loss of efficiency. Another concern arising from the aforementioned procedure is its susceptibility to under-fitting due to imperfect pre-treatment fit. It is not uncommon for the linear combination, using nonnegative weights, of pre-treatment period outcomes for the control units to inadequately approximate the pre-treatment outcomes for the treated unit. To address both of these issues, this paper proposes a simple and effective method called Synthetic Regressing Control (SRC). The SRC method begins by performing the univariate linear regression to appropriately align the pre-treatment periods of the control units with the treated unit. Subsequently, a SRC estimator is obtained by synthesizing (taking a weighted average) the fitted controls. To determine the weights in the synthesis procedure, we propose an approach that utilizes a criterion of unbiased risk estimator. Theoretically, we show that the synthesis way is asymptotically optimal in the sense of achieving the lowest possible squared error. Extensive numerical experiments highlight the advantages of the SRC method.
Individual Causal Inference Using Panel Data With Multiple Outcomes
Authors: Wei Tian
Date: 2023-06-03 00:00:00
Tasks: Causal Inference

Policy evaluation in empirical microeconomics has been focusing on estimating the average treatment effect and more recently the heterogeneous treatment effects, often relying on the unconfoundedness assumption. We propose a method based on the interactive fixed effects model to estimate treatment effects at the individual level, which allows both the treatment assignment and the potential outcomes to be correlated with the unobserved individual characteristics. This method is suitable for panel datasets where multiple related outcomes are observed for a large number of individuals over a small number of time periods. Monte Carlo simulations show that our method outperforms related methods. To illustrate our method, we provide an example of estimating the effect of health insurance coverage on individual usage of hospital emergency departments using the Oregon Health Insurance Experiment data.
The Synthetic Control Method with Nonlinear Outcomes: Estimating the Impact of the 2019 Anti-Extradition Law Amendments Bill Protests on Hong Kong's Economy
Authors: Wei Tian
Date: 2023-06-03 00:00:00

The synthetic control estimator (Abadie et al., 2010) is asymptotically unbiased assuming that the outcome is a linear function of the underlying predictors and that the treated unit can be well approximated by the synthetic control before the treatment. When the outcome is nonlinear, the bias of the synthetic control estimator can be severe. In this paper, we provide conditions for the synthetic control estimator to be asymptotically unbiased when the outcome is nonlinear, and propose a flexible and data-driven method to choose the synthetic control weights. Monte Carlo simulations show that compared with the competing methods, the nonlinear synthetic control method has similar or better performance when the outcome is linear, and better performance when the outcome is nonlinear, and that the confidence intervals have good coverage probabilities across settings. In the empirical application, we illustrate the method by estimating the impact of the 2019 anti-extradition law amendments bill protests on Hong Kong's economy, and find that the year-long protests reduced real GDP per capita in Hong Kong by 11.27% in the first quarter of 2020, which was larger in magnitude than the economic decline during the 1997 Asian financial crisis or the 2008 global financial crisis.
Physical energy cost serves as the ''invisible hand'' governing economic valuation: Direct evidence from biogeochemical data and the U.S. metal market
Authors: Zhicen Liu
Date: 2023-06-04 00:00:00

Energy supply is mandatory for the production of economic value. Nevertheless, tradition dictates that an enigmatic 'invisible hand' governs economic valuation. Physical scientists have long proposed alternative but testable energy cost theories of economic valuation, and have shown the gross correlation between energy consumption and economic output at the national level through input-output energy analysis. However, due to the difficulty of precise energy analysis and highly complicated real markets, no decisive evidence directly linking energy costs to the selling prices of individual commodities has yet been found. Over the past century, the US metal market has accumulated a huge body of price data, which for the first time ever provides us the opportunity to quantitatively examine the direct energy-value correlation. Here, by analyzing the market price data of 65 purified chemical elements (mainly metals) relative to the total energy consumption for refining them from naturally occurring geochemical conditions, we found a clear correlation between the energy cost and their market prices. The underlying physics we proposed has compatibility with conventional economic concepts such as the ratio between supply and demand or scarcity's role in economic valuation. It demonstrates how energy cost serves as the 'invisible hand' governing economic valuation. Thorough understanding of this energy connection between the human economic and the Earth's biogeochemical metabolism is essential for improving the overall energy efficiency and furthermore the sustainability of the human society.
Vocoder drift in x-vector-based speaker anonymization
Authors: Michele Panariello, Massimiliano Todisco, Nicholas Evans
Date: 2023-06-05 00:00:00
Tasks: Speaker anonymization

State-of-the-art approaches to speaker anonymization typically employ some form of perturbation function to conceal speaker information contained within an x-vector embedding, then resynthesize utterances in the voice of a new pseudo-speaker using a vocoder. Strategies to improve the x-vector anonymization function have attracted considerable research effort, whereas vocoder impacts are generally neglected. In this paper, we show that the impact of the vocoder is substantial and sometimes dominant. The vocoder drift, namely the difference between the x-vector vocoder input and that which can be extracted subsequently from the output, is learnable and can hence be reversed by an attacker; anonymization can be undone and the level of privacy protection provided by such approaches might be weaker than previously thought. The findings call into question the focus upon x-vector anonymization, prompting the need for greater attention to vocoder impacts and stronger attack models alike.
EffCRN: An Efficient Convolutional Recurrent Network for High-Performance Speech Enhancement
Authors: Marvin Sach, Jan Franzen, Bruno Defraene, Kristoff Fluyt, Maximilian Strake, Wouter Tirry, Tim Fingscheidt
Date: 2023-06-05 00:00:00
Tasks: Speech Enhancement

Fully convolutional recurrent neural networks (FCRNs) have shown state-of-the-art performance in single-channel speech enhancement. However, the number of parameters and the FLOPs/second of the original FCRN are restrictively high. A further important class of efficient networks is the CRUSE topology, serving as reference in our work. By applying a number of topological changes at once, we propose both an efficient FCRN (FCRN15), and a new family of efficient convolutional recurrent neural networks (EffCRN23, EffCRN23lite). We show that our FCRN15 (875K parameters) and EffCRN23lite (396K) outperform the already efficient CRUSE5 (85M) and CRUSE4 (7.2M) networks, respectively, w.r.t. PESQ, DNSMOS and DeltaSNR, while requiring about 94% less parameters and about 20% less #FLOPs/frame. Thereby, according to these metrics, the FCRN/EffCRN class of networks provides new best-in-class network topologies for speech enhancement.
Divided spectro-temporal attention for sound event localization and detection in real scenes for DCASE2023 challenge
Authors: Yusun Shul, Byeong-Yun Ko, Jung-Woo Choi
Date: 2023-06-05 00:00:00
Tasks: Event Detection, Sound Event Detection, Sound Event Localization and Detection

Localizing sounds and detecting events in different room environments is a difficult task, mainly due to the wide range of reflections and reverberations. When training neural network models with sounds recorded in only a few room environments, there is a tendency for the models to become overly specialized to those specific environments, resulting in overfitting. To address this overfitting issue, we propose divided spectro-temporal attention. In comparison to the baseline method, which utilizes a convolutional recurrent neural network (CRNN) followed by a temporal multi-head self-attention layer (MHSA), we introduce a separate spectral attention layer that aggregates spectral features prior to the temporal MHSA. To achieve efficient spectral attention, we reduce the frequency pooling size in the convolutional encoder of the baseline to obtain a 3D tensor that incorporates information about frequency, time, and channel. As a result, we can implement spectral attention with channel embeddings, which is not possible in the baseline method dealing with only temporal context in the RNN and MHSA layers. We demonstrate that the proposed divided spectro-temporal attention significantly improves the performance of sound event detection and localization scores for real test data from the STARSS23 development dataset. Additionally, we show that various data augmentations, such as frameshift, time masking, channel swapping, and moderate mix-up, along with the use of external data, contribute to the overall improvement in SELD performance.
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Authors: Li Fu, Siqi Li, Qingtao Li, Fangzhu Li, Liping Deng, Lu Fan, Meng Chen, Youzheng Wu, Xiaodong He
Date: 2023-06-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Self-Supervised Learning, speech-recognition, Speech Recognition

Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings. However, the advantages of SSL are gradually weakened when the amount of labeled data increases in many industrial applications. To further improve the ASR performance when abundant labels are available, we first explore the potential of combining SL and SSL ASR models via analyzing their complementarity in recognition accuracy and optimization property. Then, we propose a novel Optimal Transport based Fusion (OTF) method for SL and SSL models without incurring extra computation cost in inference. Specifically, optimal transport is adopted to softly align the layer-wise weights to unify the two different networks into a single one. Experimental results on the public 1k-hour English LibriSpeech dataset and our in-house 2.6k-hour Chinese dataset show that OTF largely outperforms the individual models with lower error rates.
End-To-End Deep Learning-based Adaptation Control for Linear Acoustic Echo Cancellation
Authors: Thomas Haubner, Andreas Brendel, Walter Kellermann
Date: 2023-06-04 00:00:00
Tasks: Acoustic echo cancellation

The attenuation of acoustic loudspeaker echoes remains to be one of the open challenges to achieve pleasant full-duplex hands free speech communication. In many modern signal enhancement interfaces, this problem is addressed by a linear acoustic echo canceler which subtracts a loudspeaker echo estimate from the recorded microphone signal. To obtain precise echo estimates, the parameters of the echo canceler, i.e., the filter coefficients, need to be estimated quickly and precisely from the observed loudspeaker and microphone signals. For this a sophisticated adaptation control is required to deal with high-power double-talk and rapidly track time-varying acoustic environments which are often faced with portable devices. In this paper, we address this problem by end-to-end deep learning. In particular, we suggest to infer the step-size for a least mean squares frequency-domain adaptive filter update by a Deep Neural Network (DNN). Two different step-size inference approaches are investigated. On the one hand broadband approaches, which use a single DNN to jointly infer step-sizes for all frequency bands, and on the other hand narrowband methods, which exploit individual DNNs per frequency band. The discussion of benefits and disadvantages of both approaches leads to a novel hybrid approach which shows improved echo cancellation while requiring only small DNN architectures. Furthermore, we investigate the effect of different loss functions, signal feature vectors, and DNN output layer architectures on the echo cancellation performance from which we obtain valuable insights into the general design and functionality of DNN-based adaptation control algorithms.
SingNet: A Real-time Singing Voice Beat and Downbeat Tracking System
Authors: Mojtaba Heydari, Ju-Chiang Wang, Zhiyao Duan
Date: 2023-06-04 00:00:00
Tasks: Downbeat Tracking

Singing voice beat and downbeat tracking posses several applications in automatic music production, analysis and manipulation. Among them, some require real-time processing, such as live performance processing and auto-accompaniment for singing inputs. This task is challenging owing to the non-trivial rhythmic and harmonic patterns in singing signals. For real-time processing, it introduces further constraints such as inaccessibility to future data and the impossibility to correct the previous results that are inconsistent with the latter ones. In this paper, we introduce the first system that tracks the beats and downbeats of singing voices in real-time. Specifically, we propose a novel dynamic particle filtering approach that incorporates offline historical data to correct the online inference by using a variable number of particles. We evaluate the performance on two datasets: GTZAN with the separated vocal tracks, and an in-house dataset with the original vocal stems. Experimental result demonstrates that our proposed approach outperforms the baseline by 3-5%.
Influence of Lossy Speech Codecs on Hearing-aid, Binaural Sound Source Localisation using DNNs
Authors: Siyuan Song, Stijn Kindt, Jasper Maes, Alexander Bohlender. Nilesh Madhu
Date: 2023-06-04 00:00:00
Tasks: Speech Enhancement

Hearing aids are typically equipped with multiple microphones to exploit spatial information for source localisation and speech enhancement. Especially for hearing aids, a good source localisation is important: it not only guides source separation methods but can also be used to enhance spatial cues, increasing user-awareness of important events in their surroundings. We use a state-of-the-art deep neural network (DNN) to perform binaural direction-of-arrival (DoA) estimation, where the DNN uses information from all microphones at both ears. However, hearing aids have limited bandwidth to exchange this data. Bluetooth low-energy (BLE) is emerging as an attractive option to facilitate such data exchange, with the LC3plus codec offering several bitrate and latency trade-off possibilities. In this paper, we investigate the effect of such lossy codecs on localisation accuracy. Specifically, we consider two conditions: processing at one ear vs processing at a central point, which influences the number of channels that need to be encoded. Performance is benchmarked against a baseline that allows full audio-exchange - yielding valuable insights into the usage of DNNs under lossy encoding. We also extend the Pyroomacoustics library to include hearing-device and head-related transfer functions (HD-HRTFs) to suitably train the networks. This can also benefit other researchers in the field.
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
Authors: Yanxiong Li, Wenchang Cao, Wei Xie, Qisheng Huang, Wenfeng Pang, Qianhua He
Date: 2023-06-03 00:00:00
Tasks: Acoustic Scene Classification, Data Augmentation, Model Compression, Scene Classification

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB.