pwc-archive/papers-with-abstracts huggingface dataset:
SURT 2.0: Advances in Transducer-based Multi-talker Speech Recognition
Authors: Desh Raj, Daniel Povey, Sanjeev Khudanpur
Date: 2023-06-18 00:00:00
Tasks: Decoder, Domain Adaptation, speech-recognition, Speech Recognition

The Streaming Unmixing and Recognition Transducer (SURT) model was proposed recently as an end-to-end approach for continuous, streaming, multi-talker speech recognition (ASR). Despite impressive results on multi-turn meetings, SURT has notable limitations: (i) it suffers from leakage and omission related errors; (ii) it is computationally expensive, due to which it has not seen adoption in academia; and (iii) it has only been evaluated on synthetic mixtures. In this work, we propose several modifications to the original SURT which are carefully designed to fix the above limitations. In particular, we (i) change the unmixing module to a mask estimator that uses dual-path modeling, (ii) use a streaming zipformer encoder and a stateless decoder for the transducer, (iii) perform mixture simulation using force-aligned subsegments, (iv) pre-train the transducer on single-speaker data, (v) use auxiliary objectives in the form of masking loss and encoder CTC loss, and (vi) perform domain adaptation for far-field recognition. We show that our modifications allow SURT 2.0 to outperform its predecessor in terms of multi-talker ASR results, while being efficient enough to train with academic resources. We conduct our evaluations on 3 publicly available meeting benchmarks -- LibriCSS, AMI, and ICSI, where our best model achieves WERs of 16.9%, 44.6% and 32.2%, respectively, on far-field unsegmented recordings. We release training recipes and pre-trained models: https://sites.google.com/view/surt2.
LM-VC: Zero-shot Voice Conversion via Speech Generation based on Language Models
Authors: Zhichao Wang, Yuanzhe Chen, Lei Xie, Qiao Tian, Yuping Wang
Date: 2023-06-18 00:00:00
Tasks: Audio Generation, Disentanglement, Language Modeling, Language Modelling, Voice Conversion

Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explore the feasibility of LMs for zero-shot voice conversion. An intuitive approach is to follow AudioLM - Tokenizing speech into semantic and acoustic tokens respectively by HuBERT and SoundStream, and converting source semantic tokens to target acoustic tokens conditioned on acoustic tokens of the target speaker. However, such an approach encounters several issues: 1) the linguistic content contained in semantic tokens may get dispersed during multi-layer modeling while the lengthy speech input in the voice conversion task makes contextual learning even harder; 2) the semantic tokens still contain speaker-related information, which may be leaked to the target speech, lowering the target speaker similarity; 3) the generation diversity in the sampling of the LM can lead to unexpected outcomes during inference, leading to unnatural pronunciation and speech quality degradation. To mitigate these problems, we propose LM-VC, a two-stage language modeling approach that generates coarse acoustic tokens for recovering the source linguistic content and target speaker's timbre, and then reconstructs the fine for acoustic details as converted speech. Specifically, to enhance content preservation and facilitates better disentanglement, a masked prefix LM with a mask prediction strategy is used for coarse acoustic modeling. This model is encouraged to recover the masked content from the surrounding context and generate target speech based on the target speaker's utterance and corrupted semantic tokens. Besides, to further alleviate the sampling error in the generation, an external LM, which employs window attention to capture the local acoustic relations, is introduced to participate in the coarse acoustic modeling.
Channel-Spatial-Based Few-Shot Bird Sound Event Detection
Authors: Lingwen Liu, Yuxuan Feng, Haitao Fu, Yajie Yang, Xin Pan, Chenlei Jin
Date: 2023-06-18 00:00:00
Tasks: Event Detection, Few-Shot Learning, Sound Classification, Sound Event Detection

In this paper, we propose a model for bird sound event detection that focuses on a small number of training samples within the everyday long-tail distribution. As a result, we investigate bird sound detection using the few-shot learning paradigm. By integrating channel and spatial attention mechanisms, improved feature representations can be learned from few-shot training datasets. We develop a Metric Channel-Spatial Network model by incorporating a Channel Spatial Squeeze-Excitation block into the prototype network, combining it with these attention mechanisms. We evaluate the Metric Channel Spatial Network model on the DCASE 2022 Take5 dataset benchmark, achieving an F-measure of 66.84% and a PSDS of 58.98%. Our experiment demonstrates that the combination of channel and spatial attention mechanisms effectively enhances the performance of bird sound classification and detection.
Autophonic Loudness of Singers in Simulated Room Acoustic Environments
Authors: Manuj Yadav, Densil Cabrera
Date: 2023-06-17 00:00:00

This paper aims to study the effect of room acoustics and phonemes on the perception of loudness of one's own voice (autophonic loudness) for a group of trained singers. For a set of five phonemes, 20 singers vocalized over several autophonic loudness ratios, while maintaining pitch constancy over extreme voice levels, within five simulated rooms. There were statistically significant differences in the slope of the autophonic loudness function (logarithm of autophonic loudness as a function of voice sound pressure level) for the five phonemes, with slopes ranging from 1.3 (/a:/) to 2.0 (/z:/). There was no significant variation in the autophonic loudness function slopes with variations in room acoustics. The autophonic room response, which represents a systematic decrease in voice levels with increasing levels of room reflections, was also studied, with some evidence found in support. Overall, the average slope of the autophonic room response for the three corner vowels (/a:/, /i:/, and /u:/) was -1.4 for medium autophonic loudness. The findings relating to the slope of the autophonic loudness function are in agreement with the findings of previous studies where the sensorimotor mechanisms in regulating voice were shown to be more important in the perception of autophonic loudness than hearing of room acoustics. However, the role of room acoustics, in terms of the autophonic room response, is shown to be more complicated, requiring further inquiry. Overall, it is shown that autophonic loudness grows at more than twice the rate of loudness growth for sounds created outside the human body.
Two simultaneous talkers distract more than one in simulated multi-talker environments, regardless of overall sound levels typical of open-plan offices
Authors: Manuj Yadav, Densil Cabrera
Date: 2023-06-17 00:00:00

The irrelevant speech effect (ISE) characterizes detriment to cognitive task performance in the presence of irrelevant speech. This paper examines whether the ISE varies due to the number of simultaneously active nearby talkers (for up to two talkers), or the overall sound level, within the context of a simulated open-plan office. Two experiments were conducted within a climate-controlled chamber that was set-up as a medium-sized open-plan office. The cognitive tasks performed by the participants included the digit recall task, and a writing task, within a room acoustic simulation of realistic multi-talker speech from spatially separated talkers. Within Experiment 1 (n=60), an increase in the number of talkers from none (T0) to one (T1), and from one to two (T2) simultaneous talkers resulted in statistically significant decline in the digit recall task performances, with effect sizes of 24% (i.e., T1 vs. T0), and 12% (i.e., T2 vs. T1), respectively. The pauses between words during the writing task were similar for T0 and T1, but showed a statistically significant increase within T2 vs. T1, with an effect size of 12%. The findings of Experiment 1 are inconsistent with the maximally distracting status attributed to T1 in some studies, but is consistent with findings in other studies. Within Experiment 2 (n = 62), the cognitive performance in T2 remained largely invariant between 45 and 57 dB (A-weighted sound pressure levels), which represents a typical range of levels within open-plan offices. In general, these findings have relevance for characterizing auditory distraction within complex multi-talker environments; both in laboratory studies and actual open-plan offices. (Abridged version; please see the paper for the full abstract)
Reliability and repeatability of ISO 3382-3 metrics based on repeated acoustic measurements in open-plan offices
Authors: Manuj Yadav, Densil Cabrera, James Love, Jungsoo Kim, Jonothan Holmes, Hugo Caldwell, Richard de Dear
Date: 2023-06-17 00:00:00

This paper investigates variability in the key ISO 3382-3:2012 metrics, based primarily on the repeatability and reliability of these metrics, using repeated measurements in open-plan offices. Two types of repeated measurements were performed in offices, Type1 (n=36), where the same path over workstations was measured from opposite ends, and Type2 (n=7), where two different measurement paths were measured. Overall, most of the Type1 results seem reasonable considering repeats were conducted in complicated room acoustic environments, while Type2 repeats would benefit from larger sample sizes in future studies. Some recommendations are outlined for the ISO 3382-3 methodology vis-a-vis Type1 and Type2 repeats, including future research directions that go beyond increased sample sizes. (This is an abridged version of the abstract. Please see the paper for the full abstract)
Low-Resource Text-to-Speech Using Specific Data and Noise Augmentation
Authors: Kishor Kayyar Lakshminarayana, Christian Dittmar, Nicola Pia, Emanuël Habets
Date: 2023-06-16 00:00:00
Tasks: Data Augmentation, text-to-speech, Text to Speech

Many neural text-to-speech architectures can synthesize nearly natural speech from text inputs. These architectures must be trained with tens of hours of annotated and high-quality speech data. Compiling such large databases for every new voice requires a lot of time and effort. In this paper, we describe a method to extend the popular Tacotron-2 architecture and its training with data augmentation to enable single-speaker synthesis using a limited amount of specific training data. In contrast to elaborate augmentation methods proposed in the literature, we use simple stationary noises for data augmentation. Our extension is easy to implement and adds almost no computational overhead during training and inference. Using only two hours of training data, our approach was rated by human listeners to be on par with the baseline Tacotron-2 trained with 23.5 hours of LJSpeech data. In addition, we tested our model with a semantically unpredictable sentences test, which showed that both models exhibit similar intelligibility levels.
Deep Learning-based Auto-encoder for Time-offset Faster-than-Nyquist Downlink NOMA with Timing Errors and Imperfect CSI
Authors: Ahmed Aboutaleb, Mohammad Torabi, Benjamin Belzer, Krishnamoorthy Sivakumar
Date: 2023-06-19 00:00:00

We examine encoding and decoding of transmitted sequences for the downlink time-offset faster than Nyquist signaling non-orthogonal multiple access NOMA (T-NOMA) channel. We employ a previously proposed singular value decomposition (SVD)-based scheme as a benchmark. While this SVD scheme provides reliable communication, our findings reveal that it is not optimal in terms of bit error rate (BER). Additionally, the SVD is sensitive to timing offset errors, and its time complexity increases quadratically with the sequence length. We propose a convolutional neural network (CNN) auto-encoder (AE) for encoding and decoding with linear time complexity. We explain the design of the encoder and decoder architectures and the training criteria. By examining several variants of the CNN AE, we show that it can achieve an excellent trade-off between performance and complexity. The proposed CNN AE surpasses the SVD method by approximately 2 dB in a T-NOMA system with no timing offset errors or channel state information estimation errors. In the presence of channel state information (CSI) error variance of 1$\%$ and uniform timing error at $\pm$4\% of the symbol interval, the proposed CNN AE provides up to 10 dB SNR gain over the SVD method. We also propose a novel modified training objective function consisting of a linear combination of the traditionally used cross-entropy (CE) loss function and a closed-form expression for the bit error rate (BER) called the Q-loss function. Simulations show that the modified loss function achieves SNR gains of up to 1 dB over the CE loss function alone.
Underactuated Two Stage CubeSat Control Law
Authors: Maxwell Cobar, Carlos Montalvo
Date: 2023-06-19 00:00:00

This paper details the implementation of a two stage underactuated control law for a CubeSat to reduce all angular rates to zero. This CubeSat has a diagonal inertia matrix where the principal axes of inertia are not equal. This is needed for momentum transfer between axes as shown by previous work in this area. The momentum transfer will provide control to the uncontrolled axis to reduce all angular rates of a CubeSat to zero. The underactuated control utilizes actuators on only two axes but still reduces all three angular rates to zero. The first stage utilizes one axis to control the uncontrolled axis to zero and then the second stage reduces the other two axes to zero. This control law is similar to other piece wise control laws but the difference lies in only using one axis to control the third axis. The control mechanism is an integrated two axis propulsion system. The control law is derived to show its viability and a fully non-linear six degree of freedom simulation tool is utilized to verify the derivation. The simulation tool is developed by the Facility of Aerospace Systems and Technology. Simulation results are shown for a 1U, 2U, and 6U CubeSat with three and two axis control. The two stage underactuated control law is compared to a proportional controller as well as a feedback linearization control law for comparison.
Attack-Resilient Design for Connected and Automated Vehicles
Authors: Tianci Yang, Carlos Murguia, Dragan Nesic, Chau Yuen
Date: 2023-06-19 00:00:00

By sharing local sensor information via Vehicle-to-Vehicle (V2V) wireless communication networks, Cooperative Adaptive Cruise Control (CACC) is a technology that enables Connected and Automated Vehicles (CAVs) to drive autonomously on the highway in closely-coupled platoons. The use of CACC technologies increases safety and the traffic throughput, and decreases fuel consumption and CO2 emissions. However, CAVs heavily rely on embedded software, hardware, and communication networks that make them vulnerable to a range of cyberattacks. Cyberattacks to a particular CAV compromise the entire platoon as CACC schemes propagate corrupted data to neighboring vehicles potentially leading to traffic delays and collisions. Physics-based monitors can be used to detect the presence of False Data Injection (FDI) attacks to CAV sensors; however, unavoidable system disturbances and modelling uncertainty often translates to conservative detection results. Given enough system knowledge, adversaries are still able to launch a range of attacks that can surpass the detection scheme by hiding within the system disturbances and uncertainty -- we refer to this class of attacks as \textit{stealthy FDI attacks}. Stealthy attacks are hard to deal with as they affect the platoon dynamics without being noticed. In this manuscript, we propose a co-design methodology (built around a series convex programs) to synthesize distributed attack monitors and $H_{\infty}$ CACC controllers that minimize the joint effect of stealthy FDI attacks and system disturbances on the platoon dynamics while guaranteeing a prescribed platooning performance (in terms of tracking and string stability). Computer simulations are provided to illustrate the performance of out tools.
New Perspectives and Systematic Approaches for Analyzing Negative Damping-Induced Sustained Oscillation
Authors: Chongbin Zhao, Qirong Jiang
Date: 2023-06-19 00:00:00

Sustained oscillations (SOs) are commonly observed in systems dominated by converters. Under specific conditions, even though the origin of SOs can be identified through negative damping modes using conventional linear analysis, utilizing the describing function to compute harmonic amplitude and frequency remains incomplete. This is because a) it can not cover the cases where hard limits are not triggered, and b) it can not provide a complete trajectory for authentic linear analysis to confirm the presence of SO. Hence, two analytical methods are proposed by returning to the essential principle of harmonic balance. a) A dedicated approach is proposed to solving steady-state harmonics via Newton-Raphson iteration with carefully chosen initial values. The method encompasses all potential hard limit triggered cases. b) By employing extended multiharmonic linearization theory and considering loop impedance, an authentic linear analysis of SO is conducted. The analysis indicates that the initial negative damping modes transform into multiple positive damping modes as SO develops. Simulation validations are performed on a two-level voltage source converter using both PSCAD and RT-LAB. Additionally, valuable insights into the work are addressed considering the modularity and scalability of the proposed methods.
Impact of Dynamic Tariffs for Smart EV Charging on LV Distribution Network Operation
Authors: Flore Verbist, Nanda Kishor Panda, Pedro P. Vergara, Peter Palensky
Date: 2023-06-19 00:00:00

With a growing share of electric vehicles (EVs) in our distribution grids, the need for smart charging becomes indispensable to minimise grid reinforcement. To circumvent the associated capacity limitations, this paper evaluates the effectiveness of different levels of network constraints and different dynamic tariffs, including a dynamic network tariff. A detailed optimisation model is first developed for public charging electric vehicles in a representative Dutch low voltage (LV) distribution network, susceptible to congestion and voltage problems by 2050 without smart charging of EVs. Later, a detailed reflection is made to assess the influence of the modelled features on the distribution system operator (DSO), charge point operator (CPO) costs, and the EVs' final state-of-charge (SOC) for both mono- (V1G) and bi-directional (V2G) charging. Results show that the dynamic network tariff outperforms other flat tariffs by increasing valley-filling. Consequently, compared to regular day-ahead pricing, a {significant} reduction in the frequency of congestion in the lines is achieved. In addition, V2G ensures the joint optimum for different stakeholders causing adequate EV user satisfaction, decreased CPO costs compared to conventional charging and fewer violations of grid constraints for the DSOs.
Short-Term Voltage Security Constrained UC to Prevent Trip Faults in High Wind Power Penetrated Power Systems
Authors: Yinhong Lin, Bin Wang, Qinglai Guo, Haotian Zhao, Hongbin Sun
Date: 2023-06-19 00:00:00
Tasks: Scheduling

For high wind power-penetrated power systems, the multiple renewable energy station short-circuit ratio (MRSCR) is often insufficient due to weak grid structures. Additionally, transient voltage sag/overvoltage issues may cause trip faults of wind turbines (WTs). Due to the time delay in WTs' controllers, it is difficult for WTs alone to meet the reactive power demands in different stages of the transient process. Some synchronous machines (SMs) must be retained through unit commitment (UC) scheduling to improve MRSCR and prevent trip faults of WTs. The MRSCR and short-term voltage security constrained-UC model is a mixed integer nonlinear programming (MINLP) problem with differential algebraic equations (DAEs) and symbolic matrix inversion, which is intractable to solve. Based on the dynamic characteristics of different devices, the original model is simplified as a general MINLP model without DAEs. Then, generalized Benders decomposition is applied to improve the solution efficiency. The relaxed MRSCR constraints are formulated in the master problem to improve the convergence, and the precise MRSCR constraints are formulated in the subproblems to consider the impact of voltage profiles. Case studies based on several benchmark systems and a provincial power grid verify the validity and efficiency of the proposed method
Dynamic Cell Modeling of Li-Ion Polymer Batteries for Precise SOC Estimation in Power-Needy Autonomous Electric Vehicles
Authors: Qasim Ajao, Lanre Sadeeq
Date: 2023-06-19 00:00:00
Tasks: Quantization

This paper presents findings on dynamic cell modeling for state-of-charge (SOC) estimation in an autonomous electric vehicle (AEV). The studied cells are Lithium-Ion Polymer-based with a nominal capacity of around 8Ah, optimized for power-needy applications. The AEV operates in a harsh environment with rate requirements up to +/-25C and highly dynamic rate profiles, unlike portable-electronic applications with constant power output and fractional C rates. SOC estimation methods effective in portable electronics may not suffice for the AEV. Accurate SOC estimation necessitates a precise cell model. The proposed SOC estimation method utilizes a detailed Kalman-filtering approach. The cell model must include SOC as a state in the model state vector. Multiple cell models are presented, starting with a simple one employing "Coulomb counting" as the state equation and Shepherd's rule as the output equation, lacking prediction of cell relaxation dynamics. An improved model incorporates filter states to account for relaxation and other dynamics in closed-circuit cell voltage, yielding better performance. The best overall results are achieved with a method combining nonlinear autoregressive filtering and dynamic radial basis function networks. The paper includes lab test results comparing physical cells with model predictions. The most accurate models obtained have an RMS estimation error lower than the quantization noise floor expected in the battery-management-system design. Importantly, these models enable precise SOC estimation, allowing the vehicle controller to utilize the battery pack's full operating range without overcharging or undercharging concerns.