pwc-archive/papers-with-abstracts huggingface dataset:
Multi-scale speaker embedding-based graph attention networks for speaker diarisation
Authors: Youngki Kwon, Hee-Soo Heo, Jee-weon Jung, You Jin Kim, Bong-Jin Lee, Joon Son Chung
Date: 2021-10-07 00:00:00
Tasks: Graph Attention

The objective of this work is effective speaker diarisation using multi-scale speaker embeddings. Typically, there is a trade-off between the ability to recognise short speaker segments and the discriminative power of the embedding, according to the segment length used for embedding extraction. To this end, recent works have proposed the use of multi-scale embeddings where segments with varying lengths are used. However, the scores are combined using a weighted summation scheme where the weights are fixed after the training phase, whereas the importance of segment lengths can differ with in a single session. To address this issue, we present three key contributions in this paper: (1) we propose graph attention networks for multi-scale speaker diarisation; (2) we design scale indicators to utilise scale information of each embedding; (3) we adapt the attention-based aggregation to utilise a pre-computed affinity matrix from multi-scale embeddings. We demonstrate the effectiveness of our method in various datasets where the speaker confusion which constitutes the primary metric drops over 10% in average relative compared to the baseline.
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models
Authors: Xiaoyu Yang, Qiujia Li, Philip C. Woodland
Date: 2021-10-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Knowledge Distillation, Language Modelling, speech-recognition, Speech Recognition

Self-supervised pre-training is an effective approach to leveraging a large amount of unlabelled data to reduce word error rates (WERs) of automatic speech recognition (ASR) systems. Since it is impractical to use large pre-trained models for many real-world ASR applications, it is desirable to have a much smaller model while retaining the performance of the pre-trained model. In this paper, we propose a simple knowledge distillation (KD) loss function for neural transducers that focuses on the one-best path in the output probability lattice under both streaming and non-streaming setups, which allows a small student model to approach the performance of the large pre-trained teacher model. Experiments on the LibriSpeech dataset show that despite being 10 times smaller than the teacher model, the proposed loss results in relative WER reductions (WERRs) of 11.5% and 6.8% on the test-other set for non-streaming and streaming student models compared to the baseline transducers trained without KD using the labelled 100-hour clean data. With an additional 860 hours of unlabelled data for KD, the WERRs increase to 48.2% and 38.5% for non-streaming and streaming students. If language model shallow fusion is used for producing distillation targets, a further improvement in the student model is observed.
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
Authors: Wanying Ge, Jose Patino, Massimiliano Todisco, Nicholas Evans
Date: 2021-10-07 00:00:00
Tasks: DeepFake Detection, Explainable artificial intelligence, Face Swapping

Substantial progress in spoofing and deepfake detection has been made in recent years. Nonetheless, the community has yet to make notable inroads in providing an explanation for how a classifier produces its output. The dominance of black box spoofing detection solutions is at further odds with the drive toward trustworthy, explainable artificial intelligence. This paper describes our use of SHapley Additive exPlanations (SHAP) to gain new insights in spoofing detection. We demonstrate use of the tool in revealing unexpected classifier behaviour, the artefacts that contribute most to classifier outputs and differences in the behaviour of competing spoofing detection models. The tool is both efficient and flexible, being readily applicable to a host of different architecture models in addition to related, different applications. All results reported in the paper are reproducible using open-source software.
Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detection
Authors: Parvaneh Janbakhshi, Ina Kodrasi
Date: 2021-10-07 00:00:00

Mainstream deep learning-based dysarthric speech detection approaches typically rely on processing the magnitude spectrum of the short-time Fourier transform of input signals, while ignoring the phase spectrum. Although considerable insight about the structure of a signal can be obtained from the magnitude spectrum, the phase spectrum also contains inherent structures which are not immediately apparent due to phase discontinuity. To reveal meaningful phase structures, alternative phase representations such as the modified group delay (MGD) and instantaneous frequency (IF) spectra have been investigated in several applications. The objective of this paper is to investigate the applicability of the unprocessed phase, MGD, and IF spectra for dysarthric speech detection. Experimental results show that dysarthric cues are present in all considered phase representations. Further, it is shown that using phase representations as complementary features to the magnitude spectrum is beneficial for deep learning-based dysarthric speech detection, with the combination of magnitude and IF spectra yielding a high performance. The presented results should raise awareness in the research community about the potential of the phase spectrum for dysarthric speech detection and motivate research into novel architectures which optimally exploit magnitude and phase information.
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
Authors: Seong-Hu Kim, Hyeonuk Nam, Yong-Hwa Park
Date: 2021-10-07 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Text-Independent Speaker Recognition, Text-Independent Speaker Verification

In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, a detailed analysis of how dynamic models work depending on phonemes is insufficient. In this paper, we propose temporal dynamic CNN (TDY-CNN) that considers temporal variation of phonemes by applying kernels optimally adapting to each time bin. These kernels adapt to time bins by applying weighted sum of trained basis kernels. Then, an analysis of how adaptive kernels work on different phonemes in various layers is carried out. TDY-ResNet-38(x0.5) using six basis kernels improved an equal error rate (EER), the speaker verification performance, by 17.3% compared to the baseline model ResNet-38(x0.5). In addition, we showed that adaptive kernels depend on phoneme groups and are more phoneme-specific at early layers. The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.
Improving Bird Classification with Unsupervised Sound Separation
Authors: Tom Denton, Scott Wisdom, John R. Hershey
Date: 2021-10-07 00:00:00
Tasks: Classification

This paper addresses the problem of species classification in bird song recordings. The massive amount of available field recordings of birds presents an opportunity to use machine learning to automatically track bird populations. However, it also poses a problem: such field recordings typically contain significant environmental noise and overlapping vocalizations that interfere with classification. The widely available training datasets for species identification also typically leave background species unlabeled. This leads classifiers to ignore vocalizations with a low signal-to-noise ratio. However, recent advances in unsupervised sound separation, such as \emph{mixture invariant training} (MixIT), enable high quality separation of bird songs to be learned from such noisy recordings. In this paper, we demonstrate improved separation quality when training a MixIT model specifically for birdsong data, outperforming a general audio separation model by over 5 dB in SI-SNR improvement of reconstructed mixtures. We also demonstrate precision improvements with a downstream multi-species bird classifier across three independent datasets. The best classifier performance is achieved by taking the maximum model activations over the separated channels and original audio. Finally, we document additional classifier improvements, including taxonomic classification, augmentation by random low-pass filters, and additional channel normalization.
AECMOS: A speech quality assessment metric for echo impairment
Authors: Marju Purin, Sten Sootla, Mateja Sponza, Ando Saabas, Ross Cutler
Date: 2021-10-06 00:00:00

Traditionally, the quality of acoustic echo cancellers is evaluated using intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ \cite{p862}, or by carrying out subjective laboratory tests. Unfortunately, the former are not well correlated with human subjective measures, while the latter are time and resource consuming to carry out. We provide a new tool for speech quality assessment for echo impairment which can be used to evaluate the performance of acoustic echo cancellers. More precisely, we develop a neural network model to evaluate call quality degradations in two separate categories: echo and degradations from other sources. We show that our model is accurate as measured by correlation with human subjective quality ratings. Our tool can be used effectively to stack rank echo cancellation models. AECMOS is being made publicly available as an Azure service.
Robust Segmentation of Cell Nuclei in 3-D Microscopy Images
Authors: Sundaresh Ram, Jeffrey J. Rodriguez
Date: 2021-10-07 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Accurate segmentation of 3-D cell nuclei in microscopy images is essential for the study of nuclear organization, gene expression, and cell morphodynamics. Current image segmentation methods are challenged by the complexity and variability of microscopy images and often over-segment or under-segment the cell nuclei. Thus, there is a need to improve segmentation accuracy and reliability, as well as the level of automation. In this paper, we propose a new automated algorithm for robust segmentation of 3-D cell nuclei using the concepts of random walk, graph theory, and mathematical morphology as the foundation. Like other segmentation algorithms, we first use a seed detection/marker extraction algorithm to find a seed voxel for each individual cell nucleus. Next, using the concept of random walk on a graph we find the probability of all the pixels in the 3-D image to reach the seed pixels of each nucleus identified by the seed detection algorithm. We then generate a 3-D response image by combining these probabilities for each voxel and use the marker controlled watershed transform on this response image to obtain an initial segmentation of the cell nuclei. Finally, we apply local region-based active contours to obtain final segmentation of the cell nuclei. The advantage of using such an approach is that it is capable of accurately segmenting highly textured cells having inhomogeneous intensities and varying shapes and sizes. The proposed algorithm was compared with three other automated nucleus segmentation algorithms for segmentation accuracy using overlap measure, Tanimoto index, Rand index, F-score, and Hausdorff distance measure. Quantitative and qualitative results show that our algorithm provides improved segmentation accuracy compared to existing algorithms.
Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods
Authors: Khaled Ardah, Martin Haardt
Date: 2021-10-07 00:00:00
Tasks: compressed sensing, Direction of Arrival Estimation

This paper proposes a compressed sensing-based high-resolution direction-of-arrival estimation method called gradient orthogonal matching pursuit (GOMP). It contains two main steps: a sparse coding approximation step using the well-known OMP method and a sequential iterative refinement step using a newly proposed gradient-descent method. To enhance the recoverability, we further propose an efficient projection matrix design method, which considers the constant modulus constraints imposed by the projection matrix hardware components. Simulation results show the effectiveness of the proposed methods as compared to benchmark algorithms.
Residual Recovery Algorithm For Modulo Sampling
Authors: Eyar Azar, Satish Mulleti, Yonina C. Eldar
Date: 2021-10-07 00:00:00

Two important attributes of analog to digital converters (ADCs) are its sampling rate and dynamic range. The sampling rate should be greater than or equal to the Nyquist rate for bandlimited signals with bounded energy. It is also desired that the signals' dynamic range should be within that of the ADC's; otherwise, the signal will be clipped. A modulo operator has been recently suggested prior to sampling to restrict the dynamic range. Due to the nonlinearity of the modulo operation, the samples are distorted. Existing recovery algorithms to recover the signal from its modulo samples operate at a high sampling rate and are not robust in the presence of noise. In this paper, we propose a robust algorithm to recover the signal from the modulo samples which operates at lower sampling rate compared to existing techniques. We also show that our method has lower error compared to existing approaches for a given sampling rate, noise level, and dynamic range of the ADC. Our results lead to less constrained hardware design to address dynamic range issues while operating at the lowest rate possible.
Climate Change Sensing through Terahertz Communications: A Disruptive Application of 6G Networks
Authors: Lasantha Thakshila Wedage, Bernard Butler, Sasitharan Balasubramaniam, Yevgeni Koucheryavy, Josep M. Jornet
Date: 2021-10-06 00:00:00

Climate change resulting from the misuse and over-exploitation of natural resources has affected and continues to impact the planet's ecosystem. This pressing issue is leading to the development of novel technologies to sense and measure damaging gas emissions. In parallel, the accelerating evolution of wireless communication networks is resulting in wider deployment of mobile telecommunication infrastructure. With 5G technologies already being commercially deployed, the research community is starting research into new technologies for 6G. One of the visions for 6G is the use of the terahertz (THz) spectrum. In this paper, we propose and explore the use of THz spectrum simultaneously for ultrabroadband communication and atmospheric sensing by leveraging the absorption of THz signals. Through the use of machine learning, we present preliminary results on how we can analyze signal path loss and power spectral density to infer the concentration of different climate-impacting gases. Our vision is to demonstrate how 6G infrastructure can provide sensor data for climate change sensing, in addition to its primary purpose of wireless communication.
Unitary Checkerboard Precoded OFDM for Low-PAPR Optical Wireless Communications
Authors: Traian E. Abrudan, Stepan Kucera, Holger Claussen
Date: 2021-10-06 00:00:00

Future 6G wireless networks will once again have to raise the capability in most of the technology domains by a factor of 10-100. Depending on the application, future requirements include peak data rates of 1Tb/s per user, 0.1ms latency, less than 1 out of a million outage, centimetre accurate positioning, near zero energy consumption at the device, and operation in different environments including factories, vehicles, and more. Optical wireless communications (OWC) have the potential to provide ultra-high data rates in a cost effective way, thanks to the vast and freely available light spectrum, and the availability of devices for transmitters and receivers. 5G NR architecture permits the integration of stand-alone OWC nodes on network layer. Current 6G research investigates advanced physical layer designs including OWC-compatible waveforms. In this context, in this paper a new pre-coded orthogonal frequency division multiplexing (OFDM) waveform is proposed that is tailored to the OWC specific needs. Its prime advantage compared to OFDM is the ultra-low peak-to-average power ratio (PAPR), while preserving other benefits, such as high spectral efficiency, flexible subcarrier nulling, and low computational complexity.
An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter
Authors: Chalukya Bhat, Aniruddh Herle, Janamejaya Channegowda, Kali Naraharisetti
Date: 2021-10-06 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Internet of Things (IoT) applications have opened up numerous possibilities to improve our lives. Most of the remote devices, part of the IoT network, such as smartphones, data loggers and wireless sensors are battery powered. It is vital to collect battery measurement data (Voltage or State-of-Charge (SOC)) from these remote devices. Presence of noise in these measurements restricts effective utilization of this dataset. This paper presents the application of Unscented Kalman Filter (UKF) to mitigate measurement noise in smartphone dataset. The simplicity of this technique makes it a constructive approach for noise removal. The datasets obtained after noise removal could be used to improve data-driven time series forecasting models which aid to accurately estimate critical battery parameters such as SOC. UKF was tested on noisy charge and discharge dataset of a smartphone. An overall Mean Squared Error (MSE) of 0.0017 and 0.0010 was obtained for Voltage charge and discharge measurements. MSE for SOC charge and discharge data measurements were 0.0018 and 0.0010 respectively.
Morphology and high frequency bio-electric fields
Authors: Johann Summhammer
Date: 2021-10-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We investigate possible shapes of the electric field, which oscillating dipoles in a certain region of biological tissue can produce in a neighboring region, or outside the tissue boundaries. We find that a wide range of shapes, including the typical morphology of limbs and appendages, can be generated as a zone of extremely low field amplitudes embedded in a zone of much larger field amplitudes. Neutral molecules with a resonance close to the frequency of the oscillating field may be attracted to this zone or be repelled from it, while the driving effect on molecules with an electric charge is only extremely weak. The forces would be sufficient for the controlled deposition of molecules during growth or regeneration. They could also serve as a method of information transfer.
Metastable dynamics of neural circuits and networks
Authors: Braden A. W. Brinkman, Han Yan, Arianna Maffei, Il Memming Park, Alfredo Fontanini, Jin Wang, Giancarlo La Camera
Date: 2021-10-06 00:00:00
Tasks: Decision Making

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.
Representation of probability distributions with implied volatility and biological rationale
Authors: Felix Polyakov
Date: 2021-09-25 00:00:00

Economic and financial theories and practice essentially deal with uncertain future. Humans encounter uncertainty in different kinds of activity, from sensory-motor control to dynamics in financial markets, what has been subject of extensive studies. Representation of uncertainty with normal or lognormal distribution is a common feature of many of those studies. For example, proposed Bayessian integration of Gaussian multisensory input in the brain or log-normal distribution of future asset price in renowned Black-Scholes-Merton (BSM) model for pricing contingent claims. Standard deviation of log(future asset price) scaled by square root of time in the BSM model is called implied volatility. Actually, log(future asset price) is not normally distributed and traders account for that to avoid losses. Nevertheless the BSM formula derived under the assumption of constant volatility remains a major uniform framework for pricing options in financial markets. I propose that one of the reasons for such a high popularity of the BSM formula could be its ability to translate uncertainty measured with implied volatility into price in a way that is compatible with human intuition for measuring uncertainty. The present study deals with mathematical relationship between uncertainty and the BSM implied volatility. Examples for a number of common probability distributions are presented. Overall, this work proposes that representation of various probability distributions in terms of the BSM implied volatility profile may be meaningful in both biological and financial worlds. Necessary background from financial mathematics is provided in the text.
The Closure of the Physical, Consciousness and Scientific Practice
Authors: Johannes Kleiner, Stephan Hartmann
Date: 2021-09-24 00:00:00

We analyse the implications of the closure of the physical for experiments in the scientific study of consciousness when all the details are considered, especially how measurement results relate to physical events. It turns out that the closure of the physical has surprising implications that conflict with scientific practice. These implications point to a fundamental flaw in the paradigm underlying many experiments conducted to date and pose a challenge to any research programme that aims to ground a physical functionalist or identity-based understanding of consciousness on empirical observations.