pwc-archive/papers-with-abstracts huggingface dataset:
CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning
Authors: Abhinav Sagar, Sai Teja Gilukara
Date: 2023-05-13 00:00:00
Tasks: Generative Adversarial Network

Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among the RRT-based algorithms is that the initial path generated is not optimal and the convergence is too slow to be used in real-world applications. In this paper, we propose a novel image-based learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention Generative Adversarial Network with a combination of spatial and channel attention and a novel loss function to design the heuristics, find a better optimal path, and improve the convergence of the algorithm both concerning time and speed. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We train and test our network on the dataset generated by \cite{zhang2021generative} and demonstrate that our algorithm outperforms the previous state-of-the-art algorithms using both the image quality generation metrics like IOU Score, Dice Score, FID score, and path planning metrics like time cost and the number of nodes. We conduct detailed experiments and ablation studies to illustrate the feasibility of our study and show that our model performs well not only on the training dataset but also on the unseen test dataset. The advantage of our approach is that we can avoid the complicated preprocessing in the state space, our model can be generalized to complicated environments like those containing turns and narrow passages without loss of accuracy, and our model can be easily integrated with other sampling-based path planning algorithms.
TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping
Authors: Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt
Date: 2023-05-18 00:00:00
Tasks: Dynamic Time Warping, Keyword Spotting

To segment a signal into blocks to be analyzed, few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size. Because of the varying lengths of different keywords or their spoken instances, choosing the right window size is a problem: A window should be long enough to contain all necessary information needed to recognize a keyword but a longer window may contain irrelevant information such as multiple words or noise and thus makes it difficult to reliably detect on- and offsets of keywords. We propose TACos, a novel angular margin loss for deriving two-dimensional embeddings that retain temporal properties of the underlying speech signal. In experiments conducted on KWS-DailyTalk, a few-shot KWS dataset presented in this work, using these embeddings as templates for dynamic time warping is shown to outperform using other representations or a sliding window and that using time-reversed segments of the keywords during training improves the performance.
Listen, Think, and Understand
Authors: Yuan Gong, Hongyin Luo, Alexander H. Liu, Leonid Karlinsky, James Glass
Date: 2023-05-18 00:00:00
Tasks: Language Modelling, Large Language Model, Music Question Answering

The ability of artificial intelligence (AI) systems to perceive and comprehend audio signals is crucial for many applications. Although significant progress has been made in this area since the development of AudioSet, most existing models are designed to map audio inputs to pre-defined, discrete sound label sets. In contrast, humans possess the ability to not only classify sounds into general categories, but also to listen to the finer details of the sounds, explain the reason for the predictions, think about what the sound infers, and understand the scene and what action needs to be taken, if any. Such capabilities beyond perception are not yet present in existing audio models. On the other hand, modern large language models (LLMs) exhibit emerging reasoning ability but they lack audio perception capabilities. Therefore, we ask the question: can we build a model that has both audio perception and a reasoning ability? In this paper, we propose a new audio foundation model, called LTU (Listen, Think, and Understand). To train LTU, we created a new OpenAQA-5M dataset consisting of 1.9 million closed-ended and 3.7 million open-ended, diverse (audio, question, answer) tuples, and have used an autoregressive training framework with a perception-to-understanding curriculum. LTU demonstrates strong performance and generalization ability on conventional audio tasks such as classification and captioning. More importantly, it exhibits emerging audio reasoning and comprehension abilities that are absent in existing audio models. To the best of our knowledge, LTU is one of the first multimodal large language models that focus on general audio (rather than just speech) understanding.
Data Augmentation for Diverse Voice Conversion in Noisy Environments
Authors: Avani Tanna, Michael Saxon, Amr El Abbadi, William Yang Wang
Date: 2023-05-18 00:00:00
Tasks: Data Augmentation, Decoder, Denoising, Voice Conversion

Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on. However, when source or target speech accents, background noise conditions, or microphone characteristics differ from training, quality voice conversion is not guaranteed. These problems are often left unexamined in VC research, giving rise to frustration in users trying to use pretrained VC models on their own data. We are interested in accent-preserving voice conversion for name pronunciation from self-recorded examples, a domain in which all three of the aforementioned conditions are present, and posit that demonstrating higher performance in this domain correlates with creating VC models that are more usable by otherwise frustrated users. We demonstrate that existing SOTA encoder-decoder VC models can be made robust to these variations and endowed with natural denoising capabilities using more diverse data and simple data augmentation techniques in pretraining.
Toxicity Inspector: A Framework to Evaluate Ground Truth in Toxicity Detection Through Feedback
Authors: Huriyyah Althunayan, Rahaf Bahlas, Manar Alharbi, Lena Alsuwailem, Abeer Aldayel, Rehab ALahmadi
Date: 2023-05-11 00:00:00

Toxic language is difficult to define, as it is not monolithic and has many variations in perceptions of toxicity. This challenge of detecting toxic language is increased by the highly contextual and subjectivity of its interpretation, which can degrade the reliability of datasets and negatively affect detection model performance. To fill this void, this paper introduces a toxicity inspector framework that incorporates a human-in-the-loop pipeline with the aim of enhancing the reliability of toxicity benchmark datasets by centering the evaluator's values through an iterative feedback cycle. The centerpiece of this framework is the iterative feedback process, which is guided by two metric types (hard and soft) that provide evaluators and dataset creators with insightful examination to balance the tradeoff between performance gains and toxicity avoidance.
Design of the Impulsive Goodwin's Oscillator: A Case Study
Authors: Alexander Medvedev, Anton V. Proskurnikov, Zhanybai T. Zhusubaliyev
Date: 2023-05-18 00:00:00

The impulsive Goodwin's oscillator (IGO) is a hybrid model composed of a third-order continuous linear part and a pulse-modulated feedback. This paper introduces a design problem of the IGO to admit a desired periodic solution. The dynamics of the continuous states represent the plant to be controlled, whereas the parameters of the impulsive feedback constitute design degrees of freedom. The design objective is to select the free parameters so that the IGO exhibits a stable 1-cycle with desired characteristics. The impulse-to-impulse map of the oscillator is demonstrated to always possess a positive fixed point that corresponds to the desired periodic solution; the closed-form expressions to evaluate this fixed point are provided. Necessary and sufficient conditions for orbital stability of the 1-cycle are presented in terms of the oscillator parameters and exhibit similarity to the problem of static output control. An IGO design procedure is proposed and validated by simulation. The nonlinear dynamics of the designed IGO are reviewed by means of bifurcation analysis. Applications of the design procedure to dosing problems in chemical industry and biomedicine are envisioned.
Detecting the driver nodes of temporal networks
Authors: Tingting Qin, Gaopeng Duan, Aming Li
Date: 2023-05-18 00:00:00

Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.
Strong Structural Controllability of Structured Networks with MIMO node systems
Authors: Yanting Ni, Xuyang Lou, Junjie Jiao, Jiajia Jia
Date: 2023-05-18 00:00:00

The article addresses the problem of strong structural controllability of structured networks with multi-input multi-output (MIMO) node systems. The authors first present necessary and sufficient conditions for strong structural controllability, which involve both algebraic and graph-theoretic aspects. These conditions are computationally expensive, especially for large-scale networks with high-dimensional state spaces. To overcome this computational complexity, we propose a necessary algebraic condition from a node system's perspective and a graph-theoretic condition from a network topology's perspective. The latter condition is derived from the structured interconnection laws and employs a new color change rule, namely weakly color change rule introduced in this paper. Overall, this article contributes to the study of strong structural controllability in structured networks with MIMO node systems, providing both theoretical and practical insights for their analysis and design.
Soft-Minimum and Soft-Maximum Barrier Functions for Safety with Actuation Constraints
Authors: Pedram Rabiee, Jesse B. Hoagg
Date: 2023-05-18 00:00:00

This paper presents two new control approaches for guaranteed safety (remaining in a safe set) subject to actuator constraints (the control is in a convex polytope). The control signals are computed using real-time optimization, including linear and quadratic programs subject to affine constraints, which are shown to be feasible. The first control method relies on a soft-minimum barrier function that is constructed using a finite-time-horizon prediction of the system trajectories under a known backup control. The main result shows that the control is continuous and satisfies the actuator constraints, and a subset of the safe set is forward invariant under the control. Next, we extend this method to allow from multiple backup controls. This second approach relies on a combined soft-maximum/soft-minimum barrier function, and it has properties similar to the first. We demonstrate these controls on numerical simulations of an inverted pendulum and a nonholonomic ground robot.
Versatile optimization-based speed-up method for autofocusing in digital holographic microscopy
Authors: Julianna Winnik, Damian Suski, Piotr Zdańkowski, Luiza Stanaszek, Vicente Micó, Maciej Trusiak
Date: 2023-05-17 00:00:00
Tasks: Position

We propose a speed-up method for the in-focus plane detection in digital holographic microscopy that can be applied to a broad class of autofocusing algorithms that involve repetitive propagation of an object wave to various axial locations to decide the in-focus position. The classical autofocusing algorithms apply a uniform search strategy, i.e., they probe multiple, uniformly distributed axial locations, which leads to heavy computational overhead. Our method substantially reduces the computational load, without sacrificing the accuracy, by skillfully selecting the next location to investigate, which results in a decreased total number of probed propagation distances. This is achieved by applying the golden selection search with parabolic interpolation, which is the gold standard for tackling single-variable optimization problems. The proposed approach is successfully applied to three diverse autofocusing cases, providing up to 136-fold speed-up.
Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)
Authors: Matias D. Cattaneo, Xinwei Ma, Yusufcan Masatlioglu
Date: 2023-05-18 00:00:00
Tasks: Decision Making

Barseghyan and Molinari (2023) give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration. A key assumption in the model is that the heterogeneity of risk preferences is unobservable but context-independent. In this comment, we build on their insights and present identification results in a setting where the risk preferences are allowed to be context-dependent.
Super-efficiency of Listed Banks in China and Determinants Analysis (2006-2021)
Authors: Yun Liao, Ruihui Xu
Date: 2023-05-18 00:00:00

This study employs the annual unbalanced panel data of 42 listed banks in China from 2006 to 2021, adopts the non-radial and non-oriented super-efficiency Data envelopment analysis (Super-SBM-UND-VRS based DEA) model considering NPL as undesired output. Our results show that the profitability super-efficiency of State-owned banks and Rural/City Commercial Banks is better than that of Joint-stock Banks. In terms of intermediary efficiency(deposit and loan), state-owned banks have advantage on other two type of banks. The determinants analysis shows that all type of banks significantly benefits from the decrease of ownership concentration which support reformation and IPO. Regional commercial banks significantly benefit from the decrease of customer concentration and the increase of reserves. On the other hand, State-owned banks should increase its loan to deposit ratio while joint-stock banks should do the opposite.
Improving Generalization Ability of Countermeasures for New Mismatch Scenario by Combining Multiple Advanced Regularization Terms
Authors: Chang Zeng, Xin Wang, Xiaoxiao Miao, Erica Cooper, Junichi Yamagishi
Date: 2023-05-18 00:00:00
Proceeding: Interspeech 2023 8
Tasks: Speech Synthesis

The ability of countermeasure models to generalize from seen speech synthesis methods to unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch scenario in which fake audio may be generated from real audio with unseen genres has not been studied thoroughly. To this end, we first use five different vocoders to create a new dataset called CN-Spoof based on the CN-Celeb1\&2 datasets. Then, we design two auxiliary objectives for regularization via meta-optimization and a genre alignment module, respectively, and combine them with the main anti-spoofing objective using learnable weights for multiple loss terms. The results on our cross-genre evaluation dataset for anti-spoofing show that the proposed method significantly improved the generalization ability of the countermeasures compared with the baseline system in the genre mismatch scenario.
Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data
Authors: Yusheng Tian, Wei Liu, Tan Lee
Date: 2023-05-18 00:00:00
Tasks: Speech Enhancement, Speech Synthesis, text-to-speech, Text to Speech

Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for text-to-speech (TTS) model training. This paper investigates the use of conditional diffusion models for generalized speech enhancement, which aims at addressing multiple types of audio degradation simultaneously. The enhancement is performed on the log Mel-spectrogram domain to align with the TTS training objective. Text information is introduced as an additional condition to improve the model robustness. Experiments on real-world recordings demonstrate that the synthetic voice built on data enhanced by the proposed model produces higher-quality synthetic speech, compared to those trained on data enhanced by strong baselines. Code and pre-trained parameters of the proposed enhancement model are available at \url{https://github.com/dmse4tts/DMSE4TTS}
Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation
Authors: Yanjie Fu, Meng Ge, Honglong Wang, Nan Li, Haoran Yin, Longbiao Wang, Gaoyan Zhang, Jianwu Dang, Chengyun Deng, Fei Wang
Date: 2023-05-18 00:00:00
Tasks: All, Speech Separation

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propose an end-to-end beamforming network for 2D location guided speech separation merely given mixture signal. It first estimates discriminable direction and 2D location cues, which imply directions the sources come from in multi views of microphones and their 2D coordinates. These cues are then integrated into location-aware neural beamformer, thus allowing accurate reconstruction of two sources' speech signals. Experiments show that our proposed model not only achieves a comprehensive decent improvement compared to baseline systems, but avoids inferior performance on spatial overlapping cases.
A Multi-Task Learning Framework for Sound Event Detection using High-level Acoustic Characteristics of Sounds
Authors: Tanmay Khandelwal, Rohan Kumar Das
Date: 2023-05-18 00:00:00
Tasks: Event Detection, Multi-Task Learning, Sound Event Detection

Sound event detection (SED) entails identifying the type of sound and estimating its temporal boundaries from acoustic signals. These events are uniquely characterized by their spatio-temporal features, which are determined by the way they are produced. In this study, we leverage some distinctive high-level acoustic characteristics of various sound events to assist the SED model training, without requiring additional labeled data. Specifically, we use the DCASE Task 4 2022 dataset and categorize the 10 classes into four subcategories based on their high-level acoustic characteristics. We then introduce a novel multi-task learning framework that jointly trains the SED and high-level acoustic characteristics classification tasks, using shared layers and weighted loss. Our method significantly improves the performance of the SED system, achieving a 36.3% improvement in terms of the polyphonic sound event detection score compared to the baseline on the DCASE 2022 Task 4 validation set.
Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech
Authors: Erica Cooper, Junichi Yamagishi
Date: 2023-05-17 00:00:00

Mean Opinion Score (MOS) is a popular measure for evaluating synthesized speech. However, the scores obtained in MOS tests are heavily dependent upon many contextual factors. One such factor is the overall range of quality of the samples presented in the test -- listeners tend to try to use the entire range of scoring options available to them regardless of this, a phenomenon which is known as range-equalizing bias. In this paper, we systematically investigate the effects of range-equalizing bias on MOS tests for synthesized speech by conducting a series of listening tests in which we progressively "zoom in" on a smaller number of systems in the higher-quality range. This allows us to better understand and quantify the effects of range-equalizing bias in MOS tests.
Improving Speaker Verification with Self-Pretrained Transformer Models
Authors: Junyi Peng, Oldřich Plchot, Themos Stafylakis, Ladislav Mošner, Lukáš Burget, Jan Černocký
Date: 2023-05-17 00:00:00
Tasks: Speaker Verification

Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle the benefits of large-scale datasets and Transformer structures from the limitations of the pre-training. In this paper, we introduce a hierarchical training approach, named self-pretraining, in which Transformer models are pretrained and finetuned on the same dataset. Three pre-trained models including HuBERT, Conformer and WavLM are evaluated on four different speaker verification datasets with varying sizes. Our experiments show that these self-pretrained models achieve competitive performance on downstream speaker verification tasks with only one-third of the data compared to Librispeech pretraining, such as VoxCeleb1 and CNCeleb1. Furthermore, when pre-training only on the VoxCeleb2-dev, the Conformer model outperforms the one pre-trained on 94k hours of data using the same fine-tuning settings.
Accelerated MR Fingerprinting with Low-Rank and Generative Subspace Modeling
Authors: Hengfa Lu, Huihui Ye, Lawrence L. Wald, Bo Zhao
Date: 2023-05-18 00:00:00
Tasks: Image Reconstruction

Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric quantitative MR imaging technique, for which image reconstruction methods utilizing low-rank and subspace constraints have achieved state-of-the-art performance. However, this class of methods often suffers from an ill-conditioned model-fitting issue, which degrades the performance as the data acquisition lengths become short and/or the signal-to-noise ratio becomes low. To address this problem, we present a new image reconstruction method for MR Fingerprinting, integrating low-rank and subspace modeling with a deep generative prior. Specifically, the proposed method captures the strong spatiotemporal correlation of contrast-weighted time-series images in MR Fingerprinting via a low-rank factorization. Further, it utilizes an untrained convolutional generative neural network to represent the spatial subspace of the low-rank model, while estimating the temporal subspace of the model from simulated magnetization evolutions generated based on spin physics. Here the architecture of the generative neural network serves as an effective regularizer for the ill-conditioned inverse problem without additional spatial training data that are often expensive to acquire. The proposed formulation results in a non-convex optimization problem, for which we develop an algorithm based on variable splitting and alternating direction method of multipliers.We evaluate the performance of the proposed method with numerical simulations and in vivo experiments and demonstrate that the proposed method outperforms the state-of-the-art low-rank and subspace reconstruction.