pwc-archive/papers-with-abstracts huggingface dataset:
Interpreting intermediate convolutional layers in unsupervised acoustic word classification
Authors: Gašper Beguš, Alan Zhou
Date: 2021-10-05 00:00:00
Tasks: Classification, regression, Time Series, Time Series Analysis

Understanding how deep convolutional neural networks classify data has been subject to extensive research. This paper proposes a technique to visualize and interpret intermediate layers of unsupervised deep convolutional networks by averaging over individual feature maps in each convolutional layer and inferring underlying distributions of words with non-linear regression techniques. A GAN-based architecture (ciwGAN arXiv:2006.02951) that includes a Generator, a Discriminator, and a classifier was trained on unlabeled sliced lexical items from TIMIT. The training process results in a deep convolutional network that learns to classify words into discrete classes only from the requirement of the Generator to output informative data. This classifier network has no access to the training data -- only to the generated data. We propose a technique to visualize individual convolutional layers in the classifier that yields highly informative time-series data for each convolutional layer and apply it to unobserved test data. Using non-linear regression, we infer underlying distributions for each word which allows us to analyze both absolute values and shapes of individual words at different convolutional layers, as well as perform hypothesis testing on their acoustic properties. The technique also allows us to test individual phone contrasts and how they are represented at each layer.
Evolutionary Algorithms for Solving Unconstrained, Constrained and Multi-objective Noisy Combinatorial Optimisation Problems
Authors: Aishwaryaprajna, Jonathan E. Rowe
Date: 2021-10-05 00:00:00
Tasks: Evolutionary Algorithms

We present an empirical study of a range of evolutionary algorithms applied to various noisy combinatorial optimisation problems. There are three sets of experiments. The first looks at several toy problems, such as OneMax and other linear problems. We find that UMDA and the Paired-Crossover Evolutionary Algorithm (PCEA) are the only ones able to cope robustly with noise, within a reasonable fixed time budget. In the second stage, UMDA and PCEA are then tested on more complex noisy problems: SubsetSum, Knapsack and SetCover. Both perform well under increasing levels of noise, with UMDA being the better of the two. In the third stage, we consider two noisy multi-objective problems (CountingOnesCountingZeros and a multi-objective formulation of SetCover). We compare several adaptations of UMDA for multi-objective problems with the Simple Evolutionary Multi-objective Optimiser (SEMO) and NSGA-II. We conclude that UMDA, and its variants, can be highly effective on a variety of noisy combinatorial optimisation, outperforming many other evolutionary algorithms.
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Authors: Girik Malik, Drew Linsley, Thomas Serre, Ennio Mingolla
Date: 2021-09-30 00:00:00
Tasks: Object, Object Recognition, Object Tracking

Nearly all models for object tracking with artificial neural networks depend on appearance features extracted from a "backbone" architecture, designed for object recognition. Indeed, significant progress on object tracking has been spurred by introducing backbones that are better able to discriminate objects by their appearance. However, extensive neurophysiology and psychophysics evidence suggests that biological visual systems track objects using both appearance and motion features. Here, we introduce $\textit{PathTracker}$, a visual challenge inspired by cognitive psychology, which tests the ability of observers to learn to track objects solely by their motion. We find that standard 3D-convolutional deep network models struggle to solve this task when clutter is introduced into the generated scenes, or when objects travel long distances. This challenge reveals that tracing the path of object motion is a blind spot of feedforward neural networks. We expect that strategies for appearance-free object tracking from biological vision can inspire solutions these failures of deep neural networks.
A Quantum Generative Adversarial Network for distributions
Authors: Amine Assouel, Antoine Jacquier, Alexei Kondratyev
Date: 2021-10-04 00:00:00
Tasks: Generative Adversarial Network

Generative Adversarial Networks are becoming a fundamental tool in Machine Learning, in particular in the context of improving the stability of deep neural networks. At the same time, recent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a particular focus on volatility modelling.
Interference suppression techniques for OPM-based MEG: Opportunities and challenges
Authors: Robert A Seymour, Nicholas Alexander, Stephanie Mellor, George C O'Neill, Tim M Tierney, Gareth R Barnes, Eleanor A Maguire
Date: 2021-10-06 00:00:00

One of the primary technical challenges facing magnetoencephalography (MEG) is that the magnitude of neuromagnetic fields is several orders of magnitude lower than interfering signals. Recently, a new type of sensor has been developed - the optically pumped magnetometer (OPM). These sensors can be placed directly on the scalp and move with the head during participant movement, making them wearable. This opens up a range of exciting experimental and clinical opportunities for OPM-based MEG experiments, including paediatric studies, and the incorporation of naturalistic movements into neuroimaging paradigms. However, OPMs face some unique challenges in terms of interference suppression, especially in situations involving mobile participants, and when OPMs are integrated with electrical equipment required for naturalistic paradigms, such as motion capture systems. Here we briefly review various hardware solutions for OPM interference suppression. We then outline several signal processing strategies aimed at increasing the signal from neuromagnetic sources. These include regression-based strategies, temporal filtering and spatial filtering approaches. The focus is on the practical application of these signal processing algorithms to OPM data. In a similar vein, we include two worked-through experiments using OPM data collected from a whole-head sensor array. These tutorial-style examples illustrate how the steps for suppressing external interference can be implemented, including the associated data and code so that researchers can try the pipelines for themselves. With the popularity of OPM-based MEG rising, there will be an increasing need to deal with interference suppression. We hope this practical paper provides a resource for OPM-based MEG researchers to build upon.
A case study on profiling of an EEG-based brain decoding interface on Cloud and Edge servers
Authors: Alexandra Samsonova, Barry J. Devereux, Georgios Karakonstantis, Lev Mukhanov
Date: 2021-10-04 00:00:00
Tasks: Brain Computer Interface, Brain Decoding, EEG, Electroencephalogram (EEG)

Brain-Computer Interfaces (BCIs) enable converting the brain electrical activity of an interface user to the user commands. BCI research studies demonstrated encouraging results in different areas such as neurorehabilitation, control of artificial limbs, control of computer environments, communication and detection of diseases. Most of BCIs use scalp-electroencephalography (EEG), which is a non-invasive method to capture the brain activity. Although EEG monitoring devices are available in the market, these devices are generally lab-oriented and expensive. Day-to-day use of BCIs is impractical at this time due to the complex techniques required for data preprocessing and signal analysis. This implies that BCI technologies should be improved to facilitate its widespread adoption in Cloud and Edge datacenters. This paper presents a case study on profiling the accuracy and performance of a brain-computer interface which runs on typical Cloud and Edge servers. In particular, we investigate how the accuracy and execution time of the preprocessing phase, i.e. the brain signal filtering phase, of a brain-computer interface varies when processing static and live streaming data obtained in real time BCI devices. We identify the optimal size of the packets for sampling brain signals which provides the best trade-off between the accuracy and performance. Finally, we discuss the pros and cons of using typical Cloud and Edge servers to perform the BCI filtering phase.
Blind Coherent Preamble Detection via Neural Networks
Authors: Jafar Mohammadi, Gerhard Schreiber, Thorsten Wild, Yejian Chen
Date: 2021-09-30 00:00:00

In wireless communications systems, the user equipment (UE) transmits a random access preamble sequence to the base station (BS) to be detected and synchronized. In standardized cellular communications systems Zadoff-Chu sequences has been proposed due to their constant amplitude zero autocorrelation (CAZAC) properties. The conventional approach is to use matched filters to detect the sequence. Sequences arrived from different antennas and time instances are summed up to reduce the noise variance. Since the knowledge of the channel is unknown at this stage, a coherent combining scheme would be very difficult to implement. In this work, we leverage the system design knowledge and propose a neural network (NN) sequence detector and timing advanced estimator. We do not replace the whole process of preamble detection by a NN. Instead, we propose to use NN only for \textit{blind} coherent combining of the signals in the detector to compensate for the channel effect, thus maximize the signal to noise ratio. We have further reduced the problem's complexity using Kronecker approximation model for channel covariance matrices, thereby, reducing the size of required NN. The analysis on timing advanced estimation and sequences detection has been performed and compared with the matched filter baseline.
DNN-assisted Particle-based Bayesian Joint Synchronization and Localization
Authors: Meysam Goodarzi, Vladica Sark, Nebojsa Maletic, Jesús Gutiérrez, Giuseppe Caire, Eckhard Grass
Date: 2021-09-29 00:00:00
Tasks: Position

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync\&loc) problem in ultra dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs' clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packet, DePF draws on the multiple signal classification algorithm that is fed by Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync\&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and thus jointly estimate the position and clock parameters of the MUs. The simulation results verifies the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of Extended Kalman filter- and linearized BRF-based joint sync\&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs, for 90$\%$of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.
Lower Interaural Coherence in Off-Signal Bands Impairs Binaural Detection
Authors: Bernhard Eurich, Jörg Encke, Stephan D. Ewert, Mathias Dietz
Date: 2021-10-06 00:00:00

Differences in interaural phase configuration between a target and a masker can lead to substantial binaural unmasking. This effect is decreased for masking noises with an interaural time difference (ITD). Adding a second noise with an opposing ITD in most cases further reduces binaural unmasking. Thus far, modeling of these detection thresholds required both a mechanism for internal ITD compensation and an increased binaural bandwidth. An alternative explanation for the reduction is that unmasking is impaired by the lower interaural coherence in off-frequency regions caused by the second masker (Marquardt & McAlpine, 2009, JASA pp. EL177 - EL182). Based on this hypothesis, the current work proposes a quantitative multi-channel model using monaurally derived peripheral filter bandwidths and an across-channel incoherence interference mechanism. This mechanism differs from wider filters since it has no effect when the masker coherence is constant across frequency bands. Combined with a monaural energy discrimination pathway, the model predicts the differences between a single delayed noise and two opposingly delayed noises, as well as four other data sets. It helps resolve the inconsistency explaining some data sets requires wide filters while others require narrow filters.
Improving Real-time Score Following in Opera by Combining Music with Lyrics Tracking
Authors: Charles Brazier, Gerhard Widmer
Date: 2021-10-06 00:00:00
Proceeding: NLP4MusA 2021 11

Fully automatic opera tracking is challenging because of the acoustic complexity of the genre, combining musical and linguistic information (singing, speech) in complex ways. In this paper, we propose a new pipeline for complete opera tracking. The pipeline is based on two trackers. A music tracker that has proven to be effective at tracking orchestral parts, will lead the tracking process. In addition, a lyrics tracker, that has recently been shown to reliably track the lyrics of opera songs, will correct the music tracker when tracking parts that have a text dominance over the music. We will demonstrate the efficiency of this method on the opera Don Giovanni, showing that this technique helps improving accuracy and robustness of a complete opera tracker.
Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet
Authors: Max Morrison, Zeyu Jin, Nicholas J. Bryan, Juan-Pablo Caceres, Bryan Pardo
Date: 2021-10-05 00:00:00
Tasks: Audio-Visual Synchronization

Modifying the pitch and timing of an audio signal are fundamental audio editing operations with applications in speech manipulation, audio-visual synchronization, and singing voice editing and synthesis. Thus far, methods for pitch-shifting and time-stretching that use digital signal processing (DSP) have been favored over deep learning approaches due to their speed and relatively higher quality. However, even existing DSP-based methods for pitch-shifting and time-stretching induce artifacts that degrade audio quality. In this paper, we propose Controllable LPCNet (CLPCNet), an improved LPCNet vocoder capable of pitch-shifting and time-stretching of speech. For objective evaluation, we show that CLPCNet performs pitch-shifting of speech on unseen datasets with high accuracy relative to prior neural methods. For subjective evaluation, we demonstrate that the quality and naturalness of pitch-shifting and time-stretching with CLPCNet on unseen datasets meets or exceeds competitive neural- or DSP-based approaches.
Unsupervised Speech Segmentation and Variable Rate Representation Learning using Segmental Contrastive Predictive Coding
Authors: Saurabhchand Bhati, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velazquez, Najim Dehak
Date: 2021-10-05 00:00:00
Tasks: Boundary Detection, Representation Learning, Self-Supervised Learning

Typically, unsupervised segmentation of speech into the phone and word-like units are treated as separate tasks and are often done via different methods which do not fully leverage the inter-dependence of the two tasks. Here, we unify them and propose a technique that can jointly perform both, showing that these two tasks indeed benefit from each other. Recent attempts employ self-supervised learning, such as contrastive predictive coding (CPC), where the next frame is predicted given past context. However, CPC only looks at the audio signal's frame-level structure. We overcome this limitation with a segmental contrastive predictive coding (SCPC) framework to model the signal structure at a higher level, e.g., phone level. A convolutional neural network learns frame-level representation from the raw waveform via noise-contrastive estimation (NCE). A differentiable boundary detector finds variable-length segments, which are then used to optimize a segment encoder via NCE to learn segment representations. The differentiable boundary detector allows us to train frame-level and segment-level encoders jointly. Experiments show that our single model outperforms existing phone and word segmentation methods on TIMIT and Buckeye datasets. We discover that phone class impacts the boundary detection performance, and the boundaries between successive vowels or semivowels are the most difficult to identify. Finally, we use SCPC to extract speech features at the segment level rather than at uniformly spaced frame level (e.g., 10 ms) and produce variable rate representations that change according to the contents of the utterance. We can lower the feature extraction rate from the typical 100 Hz to as low as 14.5 Hz on average while still outperforming the MFCC features on the linear phone classification task.
Modelling of the Fender Bassman 5F6-A Tone Stack
Authors: Steven Fenton
Date: 2021-10-05 00:00:00

This paper outlines the procedure for the effective modelling of a complex analogue filter circuit. The Fender Bassman 5F6-A is a circuit commonly employed in guitar amplifiers to shape the tonal characteristics of the amplifier output. On first inspection this circuit may look rather simple, however the controls are not orthogonal, resulting in complicated filter coefficients as the controls are varied. This in turn can make the circuit difficult to analyse without the use of mathematical emulation tools such as PSPICE or MATLAB. First the circuit is described, a method of analysis is proposed and general expressions for continuous-time coefficients are given. A MATLAB model is then produced and the frequency responses of which are shown.
On designing finite time iterative learning control based on steady state frequency response
Authors: Shuo Liu, Richard W. Longman, Benjamas Panomruttanarug
Date: 2021-10-06 00:00:00

Iterative Learning Control (ILC) is useful in spacecraft application for repeated high precision scanning maneuvers. Repetitive Control (RC) produces effective active vibration isolation based on frequency response. This paper considers ILC designed from frequency response, comparing two methods recently developed. One adapts for ILC the FIR filter design in RC that mimics the systems steady state frequency response inverse, creating a filter designed for all frequencies from zero to Nyquist. Adjustment of gains must near the beginning of the matrix need to be made because FIR gains are truncated there. The other approach uses a circulant matrix obtained from the Toeplitz matrix of Markov parameters. It is shown to give steady state frequency response for the discrete frequencies that can be seen in the number of time steps in the ILC tracking problem. The main aim is to compare their performance and ease of use. A second aim is to learn from successful ILC designs to enhances stability robustness of the two methods. Finally, the use of an ILC frequency cutoff is studied as an alternative method to eliminate instability of the control action in the converged zero tracking error solution of many ILC problems.
Modifying and optimizing the inverse of the frequency response circulant matrix as an iterative learning control compensator
Authors: Shuo Liu, Richard W. Longman
Date: 2021-10-06 00:00:00

Feedback control systems do not do what you ask. The concept of bandwidth is defined to tell what components of a command are reasonably well handled. Iterative Learning Control (ILC) seeks to converge to zero error following any given finite time desired trajectory as iterations progress. The approach can be used to achieve high precision tracking in spacecraft sensors performing repeated highly accurate sensor scanning. ILC asks for zero error for a finite time tracking maneuver, containing initial transients each iteration. The purpose of this paper is to create a method of designing ILC compensators based on steady state frequency response, and have the ILC converge to zero error in spite of transients and bandwidth. In this work the inverse of the circulant matrix of Markov parameters is used as a learning gain matrix. One can show that this matrix gives the steady state frequency response of the system at the finite number of frequencies observable in the finite data sequence of an iteration or run. Methods are used to adjust the steady state frequency response gains to address the transient part of the error signal. Numerical simulations compare the design approach to common time domain ILC design approaches, and one observes much faster convergence.
Location Method for Forced Oscillation Sources Caused by Synchronous Generators
Authors: Pablo Marchi, Pablo Gill Estevez, Cecilia Galarza
Date: 2021-10-06 00:00:00
Tasks: State Estimation

In this article, we present a new methodology to identify if forced oscillation sources are generated in excitation systems or in turbine governors. In this context, we propose to harness the information that the dynamic state estimation (DSE) provide to be able to apply a dissipating energy flow (DEF) method. The measurements that the phasor measurement units (PMUs) provide are used to estimate the internal states of different generators connected to the same bus. Here, it will be considered that the PMU at the point of connection is only capable of measuring the voltage phasor and the total current phasor. Therefore, the current injection of the generator that is causing the FO is an unobservable variable. To overcome this issue, event playback is applied to simulate the model's response and a comparison between the DSE of multiple generators is made. Finally, when the faulty generator is identified, we compute energy functions for the mechanical control loop and for the excitation control loop. The sign and rate of these energies determine the source of the oscillation, allowing that the FO can be located. Simulated signals are used to show that the proposed method provides a systematic methodology for identification and location of power systems forced oscillations than even could be non-stationary signals.
Design and Implementation of 5.8GHz RF Wireless PowerTransfer System
Authors: Je Hyeon Park, Nguyen Minh Tran, Sa Il Hwang, Dong In Kim, Kae Won Choi
Date: 2021-10-06 00:00:00

In this paper, we present a 5.8 GHz radio-frequency (RF) wireless power transfer (WPT) system that consists of 64 transmit antennas and 16 receive antennas. Unlike the inductive or resonant coupling-based near-field WPT, RF WPT has a great advantage in powering low-power internet of things (IoT) devices with its capability of long-range wireless power transfer. We also propose a beam scanning algorithm that can effectively transfer the power no matter whether the receiver is located in the radiative near-field zone or far-field zone. The proposed beam scanning algorithm is verified with a real-life WPT testbed implemented by ourselves. By experiments, we confirm that the implemented 5.8 GHz RF WPT system is able to transfer 3.67 mW at a distance of 25 meters with the proposed beam scanning algorithm. Moreover, the results show that the proposed algorithm can effectively cover radiative near-field region differently from the conventional scanning schemes which are designed under the assumption of the far-field WPT.