pwc-archive/papers-with-abstracts huggingface dataset:
Analysis and transformations of voice level in singing voice
Authors: Frederik Bous, Axel Roebel
Date: 2022-04-08 00:00:00

We introduce a neural auto-encoder that transforms the musical dynamic in recordings of singing voice via changes in voice level. Since most recordings of singing voice are not annotated with voice level we propose a means to estimate the voice level from the signal's timbre using a neural voice level estimator. We introduce the recording factor that relates the voice level to the recorded signal power as a proportionality constant. This unknown constant depends on the recording conditions and the post-processing and may thus be different for each recording (but is constant across each recording). We provide two approaches to estimate the voice level without knowing the recording factor. The unknown recording factor can either be learned alongside the weights of the voice level estimator, or a special loss function based on the scalar product can be used to only match the contour of the recorded signal's power. The voice level models are used to condition a previously introduced bottleneck auto-encoder that disentangles its input, the mel-spectrogram, from the voice level. We evaluate the voice level models on recordings annotated with musical dynamic and by their ability to provide useful information to the auto-encoder. A perceptive test is carried out that evaluates the perceived change in voice level in transformed recordings and the synthesis quality. The perceptive test confirms that changing the conditional input changes the perceived voice level accordingly thus suggesting that the proposed voice level models encode information about the true voice level.
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech
Authors: Jae-Sung Bae, Jinhyeok Yang, Tae-Jun Bak, Young-Sun Joo
Date: 2022-04-08 00:00:00
Tasks: Diversity, text-to-speech, Text to Speech

This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speaking styles. Recent advances in non-autoregressive TTS (NAR-TTS) models have significantly improved the inference speed and robustness of synthesized speech. However, the diversity of speaking styles and naturalness are needed to be improved. To solve this problem, we propose the HiMuV-TTS model that first determines the global-scale prosody and then determines the local-scale prosody via conditioning on the global-scale prosody and the learned text representation. In addition, we improve the quality of speech by adopting the adversarial training technique. Experimental results verify that the proposed HiMuV-TTS model can generate more diverse and natural speech as compared to TTS models with single-scale variational autoencoders, and can represent different prosody information in each scale.
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?
Authors: Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
Date: 2022-04-08 00:00:00
Tasks: Speaker Verification

The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler cosine similarity measure for speaker verification. Popular parametric back-ends include the probabilistic linear discriminant analysis (PLDA) and its variants. This paper investigates the properties of margin-based cross-entropy losses leading to such a shift and aims to find scoring back-ends best suited for speaker verification. In addition, we revisit the pre-processing techniques which have been widely used in the past and assess their effectiveness on large-margin embeddings. Experiments on the state-of-the-art ECAPA-TDNN networks trained with various large-margin softmax cross-entropy losses show a substantial increment in intra-speaker compactness making the conventional PLDA superfluous. In this regard, we found that constraining the within-speaker covariance matrix could improve the performance of the PLDA. It is demonstrated through a series of experiments on the VoxCeleb-1 and SITW core-core test sets with 40.8% equal error rate (EER) reduction and 35.1% minimum detection cost (minDCF) reduction. It also outperforms cosine scoring consistently with reductions in EER and minDCF by 10.9% and 4.9%, respectively.
Exploring Transformer's potential on automatic piano transcription
Authors: Longshen Ou, Ziyi Guo, Emmanouil Benetos, Jiqing Han, Ye Wang
Date: 2022-04-08 00:00:00
Tasks: Music Transcription

Most recent research about automatic music transcription (AMT) uses convolutional neural networks and recurrent neural networks to model the mapping from music signals to symbolic notation. Based on a high-resolution piano transcription system, we explore the possibility of incorporating another powerful sequence transformation tool -- the Transformer -- to deal with the AMT problem. We argue that the properties of the Transformer make it more suitable for certain AMT subtasks. We confirm the Transformer's superiority on the velocity detection task by experiments on the MAESTRO dataset and a cross-dataset evaluation on the MAPS dataset. We observe a performance improvement on both frame-level and note-level metrics after introducing the Transformer network.
SoundBeam: Target sound extraction conditioned on sound-class labels and enrollment clues for increased performance and continuous learning
Authors: Marc Delcroix, Jorge Bennasar Vázquez, Tsubasa Ochiai, Keisuke Kinoshita, Yasunori Ohishi, Shoko Araki
Date: 2022-04-08 00:00:00
Tasks: Target Sound Extraction

In many situations, we would like to hear desired sound events (SEs) while being able to ignore interference. Target sound extraction (TSE) tackles this problem by estimating the audio signal of the sounds of target SE classes in a mixture of sounds while suppressing all other sounds. We can achieve this with a neural network that extracts the target SEs by conditioning it on clues representing the target SE classes. Two types of clues have been proposed, i.e., target SE class labels and enrollment audio samples (or audio queries), which are pre-recorded audio samples of sounds from the target SE classes. Systems based on SE class labels can directly optimize embedding vectors representing the SE classes, resulting in high extraction performance. However, extending these systems to extract new SE classes not encountered during training is not easy. Enrollment-based approaches extract SEs by finding sounds in the mixtures that share similar characteristics to the enrollment audio samples. These approaches do not explicitly rely on SE class definitions and can thus handle new SE classes. In this paper, we introduce a TSE framework, SoundBeam, that combines the advantages of both approaches. We also perform an extensive evaluation of the different TSE schemes using synthesized and real mixtures, which shows the potential of SoundBeam.
Declipping of Speech Signals Using Frequency Selective Extrapolation
Authors: Markus Jonscher, Jürgen Seiler, André Kaup
Date: 2022-04-07 00:00:00
Tasks: Audio Signal Processing

The reconstruction of clipped speech signals is an important task in audio signal processing to achieve an enhanced audio quality for further processing. In this paper, Frequency Selective Extrapolation (FSE), which is commonly used for error concealment or the reconstruction of incomplete image data, is adapted to be able to restore audio signals which are distorted from clipping. For this, FSE generates a model of the signal as an iterative superposition of Fourier basis functions. Clipped samples can then be replaced by estimated samples from the model. The performance of the proposed algorithm is evaluated by using different speech test data sets. Compared to other state-of-the-art declipping algorithms, this leads to a maximum gain in SNR of up to 3:5 dB and an average gain of 1 dB.
Simplified Analysis on Filtering Sensitivity Trade-offs in Continuous- and Discrete-Time Systems
Authors: Neng Wan, Dapeng Li, Lin Song, Naira Hovakimyan
Date: 2022-04-08 00:00:00
Tasks: Sensitivity

A simplified analysis is performed on the Bode-type filtering sensitivity trade-off integrals, which capture the sensitivity characteristics of the estimate and estimation error with respect to the process input and estimated signal in continuous- and discrete-time linear time-invariant filtering systems. Compared with the previous analyses based on complex analysis and Cauchy's residue theorem, the analysis results derived from the simplified method are more explicit, thorough, and require less restrictive assumptions. For continuous-time filtering systems, our simplified analysis reveals that apart from the non-minimum phase zero sets reported in the previous literature, the value and boundedness of filtering sensitivity integrals are also determined by the leading coefficients, relative degrees, minimum phase zeros, and poles of plants and filters. By invoking the simplified method, a comprehensive analysis on the discrete-time filtering sensitivity integrals is conducted for the first time. Numerical examples are provided to verify the validity and correctness of the simplified analysis.
Optimal Lane-Free Crossing of CAVs through Intersections
Authors: Mahdi Amouzadi, Mobolaji O. Orisatoki, Arash M. Dizqah
Date: 2022-04-08 00:00:00
Tasks: Autonomous Vehicles

Connected and autonomous vehicles (CAVs), unlike conventional cars, will utilise the whole space of intersections and cross in a lane-free order. This paper formulates such a lane-free crossing of intersections as a multi-objective optimal control problem (OCP) that minimises the overall crossing time, as well as the energy consumption of CAVs. The proposed OCP is convexified by applying the dual problem theory to the constraints that avoid collision of vehicles with each other and with road boundaries. The resulting OCP is smooth and solvable by gradient-based algorithms. Simulation results show that the proposed algorithm reduces the crossing time by an average of 40% and 41% as compared to, respectively, the state-of-the-art reservation-based and lane-free methods, whilst consuming the same amount of energy. Furthermore, it is shown that the resulting crossing time of the proposed algorithm is i) fixed to a constant value regardless of the number of CAVs, and ii) very close to its theoretical limit.
Practical Prescribed-Time Seeking of a Repulsive Source by Unicycle Angular Velocity Tuning
Authors: Velimir Todorovski, Miroslav Krstic
Date: 2022-04-08 00:00:00
Tasks: Position

All the existing source-seeking algorithms for unicycle models in GPS-denied settings guarantee at best an exponential rate of convergence over an infinite interval. Using the recently introduced time-varying feedback tools for prescribed-time stabilization, we achieve source seeking in prescribed time, i.e., the convergence to a small but bounded neighborhood of the source, without the measurements of the position and velocity of the unicycle, in as short a time as the user desires, starting from an arbitrary distance from the source. The practical convergence is established using a singularly perturbed version of the Lie bracket averaging method, combined with time dilation and time contraction operations. The algorithm is robust, provably, even to bounded nonvanishing perturbations and an arbitrarily strong gradient-dependent repulsive velocity drift emanating from the source.
Stochastic Gradient-based Fast Distributed Multi-Energy Management for an Industrial Park with Temporally-Coupled Constraints
Authors: Dafeng Zhu, Bo Yang, Chengbin Ma, Zhaojian Wang, Shanying Zhu, Kai Ma, Xinping Guan
Date: 2022-04-08 00:00:00
Tasks: energy management, Management

Contemporary industrial parks are challenged by the growing concerns about high cost and low efficiency of energy supply. Moreover, in the case of uncertain supply/demand, how to mobilize delay-tolerant elastic loads and compensate real-time inelastic loads to match multi-energy generation/storage and minimize energy cost is a key issue. Since energy management is hardly to be implemented offline without knowing statistical information of random variables, this paper presents a systematic online energy cost minimization framework to fulfill the complementary utilization of multi-energy with time-varying generation, demand and price. Specifically to achieve charging/discharging constraints due to storage and short-term energy balancing, a fast distributed algorithm based on stochastic gradient with two-timescale implementation is proposed to ensure online implementation. To reduce the peak loads, an incentive mechanism is implemented by estimating users' willingness to shift. Analytical results on parameter setting are also given to guarantee feasibility and optimality of the proposed design. Numerical results show that when the bid-ask spread of electricity is small enough, the proposed algorithm can achieve the close-to-optimal cost asymptotically.
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
Authors: Chris Verhoek, Gerben I. Beintema, Sofie Haesaert, Maarten Schoukens, Roland Tó th
Date: 2022-04-08 00:00:00
Tasks: Deep Learning, Scheduling

The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite major research effort on LPV data-driven modeling, a key shortcoming of the current identification theory is that often the scheduling variable is assumed to be a given measured signal in the data set. In case of identifying an LPV model of a NL system, the selection of the scheduling map, which describes the relation to the measurable scheduling signal, is put on the users' shoulder, with only limited supporting tools available. This choice however greatly affects the usability and complexity of the resulting LPV model. This paper presents a deep-learning-based approach to provide joint estimation of a scheduling map and an LPV state-space model of a NL system from input-output data, and has consistency guarantees under general innovation-type noise conditions. Its efficiency is demonstrated on a realistic identification problem.
Stability of Non-linear Neural Feedback Loops using Sum of Squares
Authors: Matthew Newton, Antonis Papachristodoulou
Date: 2022-04-08 00:00:00

Neural network controllers have the potential to improve the performance of feedback systems compared to traditional controllers, due to their ability to act as general function approximators. However, quantifying their safety and robustness properties has proven challenging due to the non-linearities of the activation functions inside the neural network. A key robustness indicator is certifying the stability properties of the feedback system and providing a region of attraction, which has been addressed in previous literature. However, these works only address linear systems or require one to abstract the plant non-linearities and bound them using slope and sector constraints. In this paper we use a Sum of Squares programming framework to compute the stability of non-linear systems with neural network controllers directly. Within this framework, we can propose higher order candidate Lyapunov functions with richer structures that are able to better capture the dynamics of the non-linear system and the nonlinearities in the neural network. We are also able to analyse these systems in continuous time, whereas other methods rely on discretising the system. These higher order Lyapunov functions are used in conjunction with higher order multipliers on the inequality and equality constraints that bound the neural network input-output properties. The volume of the region of attraction computed is increased compared to other methods, allowing for better safety guarantees on the stability of the system. We are also able to easily analyse non-linear polynomial systems, which is not possible to do with other methods. We are also able to conduct robustness analysis on the parameter uncertainty. We show the benefits of our method using numerical examples.
Energy self-sufficient systems for monitoring sewer networks
Authors: Simon Mathis, Juan-Mario Gruber, Christian Ebi, Simon Bloem, Jörg Rieckermann, Frank Blumensaat
Date: 2022-04-07 00:00:00

Underground infrastructure networks form the backbone of vital supply and disposal systems. However, they are under-monitored in comparison to their value. This is due, in large part, to the lack of energy supply for monitoring and data transmission. In this paper, we investigate a novel, energy harvesting system used to power underground sewer infrastructure monitoring networks. The system collects the required energy from ambient sources, such as temperature differences or residual light in sewer networks. A prototype was developed that could use either a thermoelectric generator (TEG) or a solar cell to capture the energy needed to acquire and transmit ultrasonic water level data via LoRaWAN. Real-world field trials were satisfactory and showed the potential power output, as well as, possibilities to improve the system. Using an extrapolation model, we proved that the developed solution could work reliably throughout the year.
Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic
Authors: Jiawei Wang, Yang Zheng, Jianghong Dong, Chaoyi Chen, Mengchi Cai, Keqiang Li, Qing Xu
Date: 2022-04-07 00:00:00
Tasks: Autonomous Vehicles, Traffic Prediction

In this paper, we present the first experimental results of data-driven predictive control for connected and autonomous vehicles (CAVs) in dissipating traffic waves. In particular, we consider a recent strategy of Data-EnablEd Predicted Leading Cruise Control (DeeP-LCC), which bypasses the need of identifying the driving behaviors of surrounding vehicles and directly relies on measurable traffic data to achieve safe and optimal CAV control in mixed traffic. We present the implementation details of DeeP-LCC, including data collection, equilibrium estimation, and control execution. Based on a miniature experiment platform, we reproduce the phenomenon of stop-and-go waves in two typical traffic scenarios: 1) open straight-road scenario under external disturbances and 2) closed ring-road scenario with no bottlenecks. Our experiments clearly demonstrate that DeeP-LCC enables one or a few CAVs to dissipate the traffic waves in both traffic scenarios. These experimental findings validate the great potential of DeeP-LCC in smoothing practical traffic flow in the presence of noisy data, uncertain low-level vehicle dynamics, and communication and computation delays. The code and videos of our experimental results are available at https://github.com/soc-ucsd/DeeP-LCC.
Quantitative Evaluation of Common Cause Failures in High Safety-significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants
Authors: Han Bao, Hongbin Zhang, Tate Shorthill, Edward Chen, Svetlana Lawrence
Date: 2022-04-07 00:00:00

Digital instrumentation and control (DIC) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DIC (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DIC systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment, qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.
First-passage times in complex energy landscapes: a case study with nonmuscle myosin II assembly
Authors: Rick Bebon, Ulrich S. Schwarz
Date: 2022-04-07 00:00:00
Tasks: Protein Folding

Complex energy landscapes often arise in biological systems, e.g. for protein folding, biochemical reactions or intracellular transport processes. Their physical effects are often reflected in the first-passage times arising from these energy landscapes. However, their calculation is notoriously challenging and it is often difficult to identify the most relevant features of a given energy landscape. Here we show how this can be achieved by coarse-graining the Fokker-Planck equation to a master equation and decomposing its first-passage times in an iterative process. We apply this method to the electrostatic interaction between two rods of nonmuscle myosin II (NM2), which is the main molecular motor for force generation in nonmuscle cells. Energy landscapes are computed directly from the amino acid sequences of the three different isoforms. Our approach allows us to identify the most relevant energy barriers for their self-assembly into nonmuscle myosin II minifilaments and how they change under force. In particular, we find that antiparallel configurations are more stable than parallel ones, but also show more changes under mechanical loading. Our work demonstrates the rich dynamics that can be expected for NM2-assemblies under mechanical load and in general shows how one can identify the most relevant energy barriers in complex energy landscapes.
Key interaction patterns in proteins revealed by cluster expansion of the partition function
Authors: M. Tajana, A. Trovato, G. Tiana
Date: 2022-04-08 00:00:00

The native conformation of structured proteins is stabilized by a complex network of interactions. We analyzed the elementary patterns that constitute such network and ranked them according to their importance in shaping protein sequence design. To achieve this goal, we employed a cluster expansion of the partition function in the space of sequences and evaluated numerically the statistical importance of each cluster. An important feature of this procedure is that it is applied to a dense, finite system. We found that patterns that contribute most to the partition function are cycles with even numbers of nodes, while cliques are typically detrimental. Each cluster also gives a contribute to the sequence entropy, which is a measure of the evolutionary designability of a fold. We compared the entropies associated with different interaction patterns to their abundances in the native structures of real proteins.