Reviewerly/exHarmony huggingface dataset:
A Comparison Study Between the EKF and SIR-PF for GNSS/UWB Tight Integration

The tight integration of Global Navigation Satellite Systems (GNSSs) and low-cost Ultra-Wide Band (UWB) is a prospective positioning solution for autonomous mobile robots that operate in harsh environments with poor satellite visibility. Thanks to the complementarity of the two systems in terms of coverage and ranging performance, the UWB nodes can be used as anchors providing additional ranging measurements. However, the selection of the integration scheme may be a critical issue since high-accuracy positioning performance has to be traded off with the computational complexity of the implementation. This paper compares the performance of two common Bayesian filtering algorithms - the Extended Kalman Filter (EKF) and the Sequential Importance Resampling Particle Filter (SIR-PF) - for the GNSS/UWB tight integration in a dynamic environment. Considering the error sources triggered by the linear approximation employed in the EKF, simulation results show that the performance of the EKF deteriorates more than the SIR-PF when the user's kinematics changes rapidly and when the user gets close to the UWB anchor. Compared to the EKF, the SIR-PF can therefore guarantee superior positioning accuracy even if at the cost of higher computational complexity.
Canonical Voice Conversion and Dual-Channel Processing for Improved Voice Privacy of Speech Recognition Data

This paper addresses the need for enhancing the privacy of test data in a deployed automatic speech recognition (ASR) system so that what was said cannot be linked to who said it, a process we describe as acoustic de-identification. Existing techniques can be used to modify voice characteristics to make the speaker identity unrecognizable, but normally at the expense of ASR performance. We present a novel approach for improving ASR performance on acoustically de-identified voice data. Our method exploits a dual-channel input to a self-attention channel combinator front-end to an end-to-end ASR system, and data augmentation, where some amount of original speech data is used in model training. The voice data is de-identified by a zero-shot voice style transfer system to the voice of a registered, canonical speaker. We show that the proposed approach achieves a significant improvement in privacy as demonstrated by a 10x increase in the EER of an automatic speaker verification system, while also improving the ASR accuracy as demonstrated by a 18.3% reduction in WER relative to a single channel model baseline model when tested on acoustically de-identified speech.
Forecasting Graph Signals with Recursive MIMO Graph Filters

Forecasting time series on graphs is a fundamental problem in graph signal processing. When each entity of the network carries a vector of values for each time stamp instead of a scalar one, existing approaches resort to the use of product graphs to combine this multidimensional information, at the expense of creating a larger graph. In this paper, we show the limitations of such approaches, and propose extensions to tackle them. Then, we propose a recursive multiple-input multiple-output graph filter which encompasses many already existing models in the literature while being more flexible. Numerical simulations on a real world data set show the effectiveness of the proposed models.
Conditional Diffusion with Label Smoothing for Data Synthesis from Examples with Noisy Labels

Data generation techniques are critical in various fields where obtaining real-world data is difficult or expensive. However, data generators like generative adversarial networks (GANs) and diffusion-based models depend on training models using pre-existing data and labels. When the labels are unreliable, the performance of data generators can suffer. This paper proposes a novel adaptation of Denoising Diffusion Probabilistic Models (DDPM) that employs label smoothing to enhance the reliability of the generated data in the presence of label noise. Label smoothing mitigates the impact of label noise by preventing the model from becoming overconfident in mislabeled instances of data. We demonstrate that DDPM with label smoothing outperforms both conditional and unconditional DDPM in terms of the closeness of the generated data to the original data's distribution, even when the training data contains instances with mislabeled labels.
Probing Statistical Representations for End-to-End ASR

End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition. In this domain there is little research to analyse internal representation dependencies and their relationship to modelling approaches. This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA and uses these insights to identify critical parameters and improve recognition performance. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which is related to recognition performance. Altogether, this work provides analysis of the modelling approaches affecting contextual dependencies and ASR performance, and can be used to create or adapt better performing End- to-End ASR models without the requirement for hyperparameter optimisation, and also for downstream tasks.
Improved Disentangled Speech Representations Using Contrastive Learning in Factorized Hierarchical Variational Autoencoder

Leveraging the fact that speaker identity and content vary on different time scales, factorized hierarchical variational autoencoder (FHVAE) uses different latent variables to symbolize these two attributes. Disentanglement of these attributes is carried out by different prior settings of the corresponding latent variables. For the prior of speaker identity variable, FHVAE assumes it is a Gaussian distribution with an utterance-scale varying mean and a fixed variance. By setting a small fixed variance, the training process promotes identity variables within one utterance gathering close to the mean of their prior. However, this constraint is relatively weak, as the mean of the prior changes between utterances. Therefore, we introduce contrastive learning into the FHVAE framework, to make the speaker identity variables gathering when representing the same speaker, while distancing themselves as far as possible from those of other speakers. The model structure has not been changed in this work but only the training process, thus no additional cost is needed during testing. Voice conversion has been chosen as the application in this paper. Latent variable evaluations include speaker verification and identification for the speaker identity variable, and speech recognition for the content variable. Furthermore, assessments of voice conversion performance are on the grounds of fake speech detection experiments. Results show that the proposed method improves both speaker identity and content feature extraction compared to FHVAE, and has better performance than baseline on conversion.
Audio Data Augmentation for Acoustic-to-Articulatory Speech Inversion

Data augmentation has proven to be a promising prospect in improving the performance of deep learning models by adding variability to training data. In previous work with developing a noise robust acoustic-to-articulatory speech inversion (SI) system, we have shown the importance of noise augmentation to improve the performance of speech inversion in ‘noisy’ speech conditions. In this work, we extend this idea of data augmentation to improve the SI systems on both the clean speech and noisy speech data by experimenting three data augmentation methods. We also propose a Bidirectional Gated Recurrent Neural Network as the speech inversion system instead of the previously used feed forward neural network. The inversion system uses mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and six vocal tract-variables (TVs) as the output articulatory targets. The Performance of the system was measured by computing the correlation between estimated and actual TVs on the Wisconsin X-ray microbeam database. The proposed speech inversion system shows a 5% relative improvement in correlation over the baseline noise robust system for clean speech data. The pretrained model, when adapted to each unseen speaker in the test set, improves the average correlation by another 6%.
Knowledge Distillation for Efficient Audio-Visual Video Captioning

Automatically describing audio-visual content with texts, namely video captioning, has received significant attention due to its potential applications across diverse fields. Deep neural networks are the dominant methods, offering state-of-the-art performance. However, these methods are often undeployable in low-power devices like smartphones due to the large size of the model parameters. In this paper, we propose to exploit simple pooling front-end and down-sampling algorithms with knowledge distillation for audio and visual attributes using a reduced number of audio-visual frames. With the help of knowledge distillation from the teacher model, our proposed method greatly reduces the redundant information in audio-visual streams without losing critical contexts for caption generation. Extensive experimental evaluations on the MSR-VTT dataset demonstrate that our proposed approach significantly reduces the inference time by about 80% with a small sacrifice (less than 0.02%) in captioning accuracy.
GreekPolitics: Sentiment Analysis on Greek Politically Charged Tweets

The rapid growth of on-line social media platforms has rendered opinion mining/sentiment analysis a critical area of Natural Language Processing (NLP) research. This paper focuses on analyzing Twitter posts (tweets), written in the Greek language and politically charged in content. This is a rather underexplored topic, due to the scarcity of publicly available annotated datasets. Thus, we present and release “GreekPolitics”, i.e., a dataset of Greek tweets with politically charged content, independently annotated across four different sentiment dimensions: polarity, figurativeness, aggressiveness and bias. GreekPolitics has been evaluated comprehensively in a classification setting, separately for each sentiment, using state-of-the-art Deep Neural Networks (DNNs) and data augmentation methods. This paper details the dataset, the evaluation process and the experimental results. Based on these, best practices are identified for achieving the highest classification accuracy at the test stage.
Topic Identification for Spontaneous Speech: Enriching Audio Features with Embedded Linguistic Information

Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approaches work well in high-resource scenarios, where there are sufficient data to train both components of the pipeline. However, in low-resource situations, the ASR system, even if available, produces low-quality transcripts, leading to a bad text-based classifier. Moreover, spontaneous speech containing hesitations can further degrade the performance of the ASR model. In this paper, we investigate alternatives to the standard text-only solutions by comparing audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available, while the hybrid multi-modal solutions achieve the best results.
Graph-Time Trend Filtering and Unrolling Network

Reconstructing missing values and removing noise from network-based multivariate time series requires developing graph-time regularizers capable of capturing their spatiotemporal behavior. However, current approaches based on joint spatiotemporal smoothness, diffusion, or variations thereof may not be effective for time series with discontinuities across the graph or time. To address this challenge, we propose a joint graph-time trend filter operating over a product graph representing spatiotemporal relations. Additionally, we develop a graph-time unrolled neural network to learn the prior from the data, which is based on the alternating direction method of multipliers iterations of the graph-time trend filter and on graph-time convolutional filters. Numerical tests with two synthetic and four real datasets corroborate the effectiveness of both approaches, highlight their inherent trade-offs, and show they compare well with state-of-the-art alternatives.
Sparsity-Aware Block Diagonal Representation for Subspace Clustering

A block diagonally structured affinity matrix is an informative prior for subspace clustering which embeds the data points in a union of low-dimensional subspaces. Structuring a block diagonal matrix can be challenging due to the determination of an appropriate sparsity level, especially when outliers and heavy-tailed noise obscure the underlying subspaces. We propose a new sparsity-aware block diagonal representation (SABDR) method that robustly estimates the appropriate sparsity level by leveraging upon the geometrical analysis of the low-dimensional structure in spectral clustering. Specifically, we derive the Euclidean distance between the embeddings of different clusters to develop a computationally efficient density-based clustering algorithm. In this way, the sparsity parameter selection problem is re-formulated as a robust approximation of target between-clusters distances. Comprehensive experiments using real-world data demonstrate the effectiveness of SABDR in different subspace clustering applications.
Rescaling of Symbol Counts for Adaptive rANS Coding

The abbreviation rANS stands for a relatively new method of arithmetic coding based on asymmetric numeral systems (ANS) which combines the advantages of arithmetic coding in terms of performance and the advantages of Huffman coding in terms of speed. Compared to conventional arithmetic coding methods, the mathematical apparatus is slightly different which has the consequence that the decoding order is reversed to the encoding order, i.e. the processing follows the last-in-first-out principle. This makes it somewhat difficult to design the coding process to adapt to changing symbol statistics, and therefore rANS coding has so far only been applied in settings with fixed statistics. In particular, the frequent rescaling of statistics required to reduce the influence of old symbols becomes a problem when the order of processing is different on the encoder and decoder sides. This paper proposes a new method that allows adaptive coding within the framework of rANS coding and additionally offers the possibility of rescaling the symbols frequencies. Investigations show that this method enables the same compression performance for rANS as for conventional arithmetic coding.
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection

Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining.
Voice Processing for Police Hotlines

This paper explores the potential of applying machine learning techniques to enhance the capabilities of emergency services operation. The study examines the preprocessing techniques employed and delves into the application of speaker diarization for identifying multiple speakers in audio input, comparing the performance of different models in this task. The authors develop a custom speaker diarization model tailored to specific scenarios, capable of effectively handling voice recordings with significant speaker changes. Additionally, the study investigates various machine learning models for speech recognition, ultimately selecting XLSR- Wav2vec2, a model published by Facebook, for further analysis. The chosen model exhibits high accuracy in identifying spoken words and facilitates the automation of dataset creation in both Arabize (a combination of Latin script and Arabic numerals) and Arabic languages. The findings of this study contribute to the advancement of machine learning in call center applications, particularly in speech analysis and diarization tasks. The presented results shed light on the feasibility and performance of machine learning techniques in the hotlines response domain, offering valuable insights for researchers and practitioners.
Reinforcement Learning at the Forefront of Robot Path Planning

Abstract-Robots are used in many different domains to accomplish tasks. Irrespective of the type of task assigned, a robot needs to move from one place to another to complete its task. While moving, it needs to avoid obstacles in its path. This means that path planning for the robot is required. There are different methods available for robot path planning. This paper proposes a path planning method that uses reinforcement learning to predict the turning angle of a robot in a customised environment. A turning angle is used by a robot to deviate from its current path to avoid an obstacle. A deep deterministic policy gradient agent is trained in the custom environment that consists of the proposed path learning method. The agent predicts the turning angle, which is used by the environment to update a robot’s position. The environment generates observation and reward, which are given back to the agent for angle prediction and policy update, respectively. The proposed method has been tested in three unknown environments. The results show that a proper obstacle free path was planned. Also, the proposed method was applied to tractor-trailer robotic system. The method was used to generates points and the tractor-trailer robot moved from previous point to the generated point using kinematic equations. The proposed method was evaluated for its performance in 200 different scenarios obtaining a success rate of $79 \%$.
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)

Unmanned Aerial Vehicles (UAVs) are gaining significant scientific interest in critical infrastructure inspection due to their flexibility, cost-effectiveness and advanced computer vision capabilities. This research focuses on high-voltage powerline surveillance, where automatic inspection is a priority for grid companies to prevent power failures. To address the need for real-time detection with limited computational power, we evaluate the recently developed object detection algorithm, YOLOvS. We propose a fine-tuned model trained on a custom dataset to detect key components, i. e. towers, insulators and conductors. The proposed method achieves an overall accuracy rate of 82.3% (mAp@O.S) and enables real-time detection, demonstrating its suitability for inspection tasks and visual-based navigation. Our model was also tested on a custom-built quadcopter with an Nvidia Jetson Nano (4GB) on board, achieving a frame rate of 33fps on live video under real environmental conditions.
Performance Evaluation of 1 MW On-grid Solar Photovoltaic Plant with Single Axis Tracker in Muscat, Oman

This research paper presents data comparison of two different simulation software with an actual on-grid results of 1 MW bifacial solar plant under real meteorological conditions. The solar PV plant of 1 MW incorporated with single axis tracker is one of the largest installed capacities in one single location for an oil and gas company in Muscat, Oman. The main objective of this research is to present and examine the actual results obtained from the plant and compare it with simulated results in a coastal environment. In addition, promote the use of solar photovoltaic plants as a clean and renewable energy to replace fossil fuels. The research shows the actual CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> savings and bill reduction percentage after integration of 1 MW into the grid. This approach relies on a genuine atmosphere condition including, correspondence system and meteorological station. The data analysis evaluation in this study is carried out for a period of one full year (2022). The site receives very good solar insolation and temperature with an average of 7056 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /month and 35.6 °C, respectively. The integration of 1MW solar PV results in 3% bill reduction for the same year. The plant experienced a very high annual average performance ratio of 90.40%. The energy yield simulations are made on two broad used PV design software: PVsyst and Helioscope. By comparing the simulated results with the actual values of the plant, it was found that PVsyst results are in good agreement with the actual produced results. This research provides the first time comparative study for a 1 MW solar plant with a single axis tracker at Muscat. The actual results show a significant power generation output compared to the simulated results despite the harsh costal environment and high humidity observed at site.
COVID-SR: A Priori Knowledge-Based Multi-Scale Fusion Super-Resolution Network For COVID-19 CT Images

The new coronavirus (COVID-19) pandemic poses a serious risk to the public's health and continues to have a negative influence on the economy. Currently, computed tomography (CT) imaging is the most effective tool for examining COVID-19 lung infections in medical imaging technology. The diagnosis of COVID-19 is aided by high-resolution medical photographs since they can show more distinct anatomical characteristics of the human body. However, obtaining clear high-resolution medical images is challenging due to factors such as the imaging environment, system limitations and human error. Therefore, we propose a deep multi-scale fusion network based on prior knowledge to achieve super-resolution reconstruction of COVID-19 CT images. The network comprises cascaded multi-scale residual blocks that fully extract multi-receptive field ratio features of CT images and leverage the ability of the network to extract deep image semantic features at different scales. Additionally, CT image texture changes reflect internal organ tissue changes; fine texture features are more helpful for networks to recover detailed image information; therefore, we inject prior knowledge into the learning process by using non-sampling contourlet transform (NSCT) to combine NSCT high-frequency information with images so that our network can learn finer texture features and maintain richer details than spatial domain information during training. Experiment results on two publicly available COVID-19 CT image datasets show that our suggested method achieves greater PSNR and SSIM values while better preserving precise texture information when compared to existing cutting-edge super-resolution technologies.