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Department Of Multimedia Systems
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Catalog Publications
Year 2025
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Anterior prefrontal EEG theta activities indicate memory and executive functions in epilepsy patients
PublicationObjective: Cognitive deficits are one of the most debilitating comorbidities in epilepsy and other neurodegenerative, neuropsychiatric, and neurodevelopmental brain disorders. Current diagnostic and therapeutic options are limited and lack objective measures of the underlying neural activities. In this study, electrophysiological biomarkers that reflect cognitive functions in clinically validated batteries were determined to aid...
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Determining a damping coefficient by using a microelectromechanical systems accelerometer for finite element method simulation purposes on a discrete damper example
PublicationThis paper introduces a methodology, algorithms, and the achieved results for determining the damping coefficient using a microelectromechanical systems (MEMS) accelerometer, specifically for finite element method (FEM) simulations. The paper not only showcases the results but also employs several independent verification procedures to confirm the validity of the obtained damping coefficient. The determined damping coefficient...
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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson’s disease classification
PublicationThis study investigates the classification of individuals as healthy or at risk of Parkinson’s disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are created from an original dataset: (normal dataset), ( subjected to Canny edge detection and Hessian filtering), (augmented ), and (augmented ). We evaluate a range of ML models-Logistic...
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Machine learning tools match physician accuracy in multilingual text annotation
PublicationIn the medical field, text annotation involves categorizing clinical and biomedical texts with specific medical categories, enhancing the organization and interpretation of large volumes of unstructured data. This process is crucial for developing tools such as speech recognition systems, which help medical professionals reduce their paperwork. It addresses a significant cause of burnout reported by up to 60% of medical staff....
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Nanotechnology meets radiobiology: Fullerenols and Metallofullerenols as nano-shields in radiotherapy
PublicationDespite significant advances in the development of radioprotective measures, the clinical application of radioprotectors and radiomitigators remains limited due to insufficient efficacy and high toxicity of most agents. Additionally, in oncological radiotherapy, these compounds may interfere with the therapeutic effectiveness. Recent progress in nanotechnology highlights fullerenols (FulOHs) and metallofullerenols (Me@FulOHs) as...
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Separation of Simultaneous Speakers with Acoustic Vector Sensor
PublicationThis paper presents a method of sound source separation in live audio signals, based on sound intensity analysis. Sound pressure signals recorded with an acoustic vector sensor are analyzed, and the spectral distribution of sound intensity in two dimensions is calculated. Spectral components of the analyzed signal are selected based on the calculated source direction, which leads to a spatial filtration of the sound. The experiments...
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Speech intelligibility improvement for public address systems in noisy environments based on automatic gain selection in octave bands
PublicationA method of speech intelligibility improvement in public address (PA) systems installed in reverberant acoustic spaces is proposed. The method is mainly intended for systems operating in high background noise levels. The algorithm is based on the near end listening enhancement approach. Signal-to-noise ratio (SNR) is evaluated in the octave frequency bands. Signal levels in the acoustic space are estimated using the measured impulse...
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The Impact of 8- and 4-Bit Quantization on the Accuracy and Silicon Area Footprint of Tiny Neural Networks
PublicationIn the field of embedded and edge devices, efforts have been made to make deep neural network models smaller due to the limited size of the available memory and the low computational efficiency. Typical model footprints are under 100 KB. However, for some applications, models of this size are too large. In low-voltage sensors, signals must be processed, classified or predicted with an order of magnitude smaller memory. Model downsizing...
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Vehicle Type Recognition Based on Audio Data
PublicationIdentifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the...
Year 2024
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3-D Printable Metal-Dielectric Metasurface for Risley Prism-Based Beam-Steering Antennas
PublicationA 3-D printable, planar, metal-dielectric metasurface-based, 2-D beam-steering system for aperture-type antennas is presented in this paper. This beam steering system, also known as the near-field meta-steering system, comprises two fully passive phase-gradient metasurfaces placed in the antenna’s nearfield region to steer the radiation beam. To address the non-uniform electric field phase of the aperture antenna, phase correction...
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A Comparison of Directional Beamforming Capabilities: High-Order Ambisonic Microphone vs. Shotgun Microphones
PublicationThis article presents the practical implications of the directional beamforming capability of a higher-order ambisonic microphone compared with popular shotgun microphones. Five different microphones were used in the study: Sennheiser MKH 416, Rode NTG2, Panasonic AG-MC200, Zoom SGH-6, and Zylia ZM-1 (ambisonic microphone). The results highlight the versatility of higher-order ambisonics for non-immersive use, which allows for...
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A Mammography Data Management Application for Federated Learning
PublicationThis study aimed to develop and assess an application designed to enhance the management of a local client database consisting of mammographic images with a focus on ensuring that images are suitably and uniformly prepared for federated learning applications. The application supports a comprehensive approach, starting with a versatile image-loading function that supports DICOM files from various medical imaging devices and settings....
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Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
PublicationIn this work, we investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy, aiming to prevent forgetting. KDbased methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Our analysis reveals that this issue originates from substantial representation shifts in the teacher...
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Category Adaptation Meets Projected Distillation in Generalized Continual Category Discovery
Publication"Generalized Continual Category Discovery (GCCD) tackles learning from sequentially arriving, partially labeled datasets while uncovering new categories. Traditional methods depend on feature distillation to prevent forgetting the old knowledge. However, this strategy restricts the model’s ability to adapt and effectively distinguish new categories. To address this, we introduce a novel technique integrating a learnable projector...
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Comparison of ambisonic and object-based spatial sound recording techniques
PublicationThis article presents a comparison of spatial sound recording techniques based on scene-based and object-based audio. The study aimed to make different mixes from a recording which consists of a higher-order ambisonic microphone and spot microphones. For spot microphones simple ambisonics encoding was used, which allows panning the individual channels on an ambisonic sphere as objects. Recordings were combined in various variants...
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Decision-making under stress: A psychological and neurobiological integrative model
PublicationUnderstanding the impact of stress on cognitive processes, particularly decision-making, is crucial as it underpins behaviors essential for survival. However, research in this domain has yielded disparate results, with inconsistencies evident across stress-induction paradigms and drug administration protocols designed to investigate specific stress pathways or neuromodulators. Building upon empirical studies, this research identifies...
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Decoding imagined speech for EEG-based BCI
PublicationBrain–computer interfaces (BCIs) are systems that transform the brain's electrical activity into commands to control a device. To create a BCI, it is necessary to establish the relationship between a certain stimulus, internal or external, and the brain activity it provokes. A common approach in BCIs is motor imagery, which involves imagining limb movement. Unfortunately, this approach allows few commands. As an alternative, this...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublicationBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Developing a Low SNR Resistant, Text Independent Speaker Recognition System for Intercom Solutions - A Case Study
PublicationThis article presents a case study on the development of a biometric voice verification system for an intercom solution, utilizing the DeepSpeaker neural network architecture. Despite the variety of solutions available in the literature, there is a noted lack of evaluations for "text-independent" systems under real conditions and with varying distances between the speaker and the microphone. This article aims to bridge this gap....
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Divide and not forget: Ensemble of selectively trained experts in Continual Learning
PublicationClass-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation,...
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Do stress hormones influence choice? A systematic review of pharmacological interventions on the HPA axis and/or SAM system
PublicationThe hypothalamus–pituitary–adrenal axis (HPA axis) and the sympathetic–adrenal–medullary system (SAM system), two neuroendocrine systems associated with the stress response, have often been implicated to modulate decision-making in various domains. This systematic review summarizes the scientific evidence on the effects of pharmacological HPA axis and SAM system modulation on decision-making. We found 6375 references, of which...
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English Language Learning Employing Developments in Multimedia IS
PublicationIn the realm of the development of information systems related to education, integrating multimedia technologies offers novel ways to enhance foreign language learning. This study investigates audio-video processing methods that leverage real-time speech rate adjustment and dynamic captioning to support English language acquisition. Through a mixed-methods analysis involving participants from a language school, we explore the impact...
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Exploring music listening patterns: an online survey
PublicationAn online survey was carried out to explore how respondents listen to music recordings. It was anticipated that the listener’s preferences would be influenced by various factors, such as age, music genre, the contexts in which they listen, and their favored methods of music consumption. Consequently, the data were collected to analyze these relationships. The survey, structured as a web application, encompassed 23 questions,...
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Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model
PublicationPresentation Attack Detection (PAD) is crucial in biometric finger vein recognition. The susceptibility of these systems to forged finger vein images is a significant challenge. Existing approaches to mitigate presentation attacks have computational complexity limitations and limited data availability. This study proposed a novel method for identifying presentation attacks in finger vein biometric systems. We have used optimal...
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High frequency oscillations in human memory and cognition: a neurophysiological substrate of engrams?
PublicationDespite advances in understanding the cellular and molecular processes underlying memory and cognition, and recent successful modulation of cognitive performance in brain disorders, the neurophysiological mechanisms remain underexplored. High frequency oscillations beyond the classic electroencephalogram spectrum have emerged as a potential neural correlate of fundamental cognitive processes. High frequency oscillations are detected...
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Identyfikacja instrumentu muzycznego z nagrania fonicznego za pomocą sztucznych sieci neuronowych
PublicationCelem rozprawy jest zbadanie algorytmów do identyfikacji instrumentów występujących w sygnale polifonicznym z wykorzystaniem sztucznych sieci neuronowych. W części teoretycznej przywołano podstawy przetwarzania sygnałów fonicznych w kontekście ekstrakcji parametrów sygnałów wykorzystywanych w treningu sieci neuronowych. Dodatkowo dokonano analizy rozwoju metod uczenia maszynowego z uwzględnieniem podziału na sieci neuronowe pierwszej,...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Learning sperm cells part segmentation with class-specific data augmentation
PublicationInfertility affects around 15% of couples worldwide. Male fertility problems include poor sperm quality and low sperm count. The advanced fertility treatment methods like ICSI are nowadays supported by vision systems to assist embryologists in selecting good quality sperm. Computer-Assisted Semen Analysis (CASA) provides quantitative and qualitative sperm analysis concerning concentration, motility, morphology, vitality, and fragmentation....
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Leveraging Activation Maps for Improved Acoustic Events Detection and Classification
PublicationThis paper presents a novel approach to enhance the accuracy of deep learning models for acoustic event detection and classification in real-world environments. We introduce a method that leverages activation maps to identify and address model overfitting, combined with an expert-knowledge-based event detection algorithm for data pre-processing. Our approach significantly improved classification performance, increasing the F1 score...
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Looking through the past: better knowledge retention for generative replay in continual learning
PublicationIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained...
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MagMax: Leveraging Model Merging for Seamless Continual Learning
PublicationThis paper introduces a continual learning approach named MagMax, which utilizes model merging to enable large pre-trained models to continuously learn from new data without forgetting previously acquired knowledge. Distinct from traditional continual learning methods that aim to reduce forgetting during task training, MagMax combines sequential fine-tuning with a maximum magnitude weight selection for effective knowledge integration...
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Missing Puzzle Pieces in Dementia Research: HCN Channels and Theta Oscillations
PublicationIncreasing evidence indicates a role of hyperpolarization activated cation (HCN) channels in controlling the resting membrane potential, pacemaker activity, memory formation, sleep, and arousal. Their disfunction may be associated with the development of epilepsy and age-related memory decline. Neuronal hyperexcitability involved in epileptogenesis and EEG desynchronization occur in the course of dementia in human Alzheimer’s Disease...
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Mobilenet-V2 Enhanced Parkinson's Disease Prediction with Hybrid Data Integration
PublicationThis study investigates the role of deep learning models, particularly MobileNet-v2, in Parkinson's Disease (PD) detection through handwriting spiral analysis. Handwriting difficulties often signal early signs of PD, necessitating early detection tools due to potential impacts on patients' work capacities. The study utilizes a three-fold approach, including data augmentation, algorithm development for simulated PD image datasets,...
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Opracowanie metodologii rozpoznawania i klasyfikowania emocji w filmach przy użyciu sztucznych sieci neuronowych
PublicationCelem rozprawy doktorskiej jest opracowanie metodologii pozwalającej na rozpoznawanie i klasyfikację emocji w filmie za pomocą sztucznych sieci neuronowych. W pracy przedstawiono tematykę związaną z kolorowaniem sceny filmowej w kontekście oddziaływania koloru na emocje widza. W celu analizy wpływu filmow na emocje widza dokonano wyboru tytułow filmowych, następnie przeprowadzono szereg wstępnych testow subiektywnych pozwalających...
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Reverberation divergence in VR applications
PublicationThis project aimed to investigate the correlation between virtual reality (VR) imagery and ambisonic sound. With the increasing popularity of VR applications, understanding how sound is perceived in virtual environments is crucial for enhancing the immersiveness of the experience. In the experiment, participants were immersed in a virtual environment that replicated a concert hall. Their task was to assess the correspondence between...
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Revisiting serotonin’s role in spatial memory: A call for sensitive analytical approaches
PublicationThe serotonergic system is involved in various psychiatric and neurological conditions, with serotonergic drugs often used in treatment. These conditions frequently affect spatial memory, which can serve as a model of declarative memory due to well-known cellular components and advanced methods that track neural activity and behavior with high temporal resolution. However, most findings on serotonin's effects on spatial learning...
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Revisiting Supervision for Continual Representation Learning
Publication"In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing interest in unsupervised continual learning, which makes use of the vast amounts of unlabeled data. Recent studies have highlighted the strengths of unsupervised methods, particularly self-supervised learning, in providing robust representations. The improved...
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Session-Based Recommendation with Graph Neural Networks with an Examination of the Impact of Local and Global Vectors
PublicationThis study investigates the application of graph neural networks (GNN) in session-based recommendation systems (SR), focusing on a key modification involving the use of a global vector. Session-based recommendation systems often face challenges in accurately capturing user behavior due to the limited data available within individual sessions. The SR-GNN model, originally designed for automatic feature extraction from session graphs...
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Sounding Mechanism of a Flue Organ Pipe—A Multi-Sensor Measurement Approach
PublicationThis work presents an approach that integrates the results of measuring, analyzing, and modeling air flow phenomena driven by pressurized air in a flue organ pipe. The investigation concerns a Bourdon organ pipe. Measurements are performed in an anechoic chamber using the Cartesian robot equipped with a 3D acoustic vector sensor (AVS) that acquires both acoustic pressure and air particle velocity. Also, a high-speed camera is employed...
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Task-recency bias strikes back: Adapting covariances in Exemplar-Free Class Incremental Learning
PublicationExemplar-Free Class Incremental Learning (EFCIL) tackles the problem of training a model on a sequence of tasks without access to past data. Existing state-of-the-art methods represent classes as Gaussian distributions in the feature extractor's latent space, enabling Bayes classification or training the classifier by replaying pseudo features. However, we identify two critical issues that compromise their efficacy when the feature...
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The Impact of Foreign Accents on the Performance of Whisper Family Models Using Medical Speech in Polish
PublicationThe article presents preliminary experiments investigating the impact of accent on the performance of the Whisper automatic speech recognition (ASR) system, specifically for the Polish language and medical data. The literature review revealed a scarcity of studies on the influence of accents on speech recognition systems in Polish, especially concerning medical terminology. The experiments involved voice cloning of selected individuals...
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Vehicle type recognition: a case study of MobileNetV2 for an image Classification task
PublicationThe goal of the research was to demonstrate the full data science lifecycle through a use case of the MobileNetv2 model for vehicle image Classification task using various validation and test sets, each with different difficulty level. Diverse model variations were employed, each designed to recognize images of ground vehicles and classify them into one of five possible classes: car, truck, motorcycle, bicycle, or bus. In terms...
Year 2023
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A commonly-accessible toolchain for live streaming music events with higher-order ambisonic audio and 4k 360 vision
PublicationAn immersive live stream is especially interesting in the ongoing development of telepresence tools, especially in the virtual reality (VR) or mixed reality (MR) domain. This paper explores the remote and immersive way of enabling telepresence for the audience to high-fidelity music performance using freely-available and easily-accessible tools. A functional VR live-streaming toolchain, comprising 360 vision and higher-order ambisonic...
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A survey of automatic speech recognition deep models performance for Polish medical terms
PublicationAmong the numerous applications of speech-to-text technology is the support of documentation created by medical personnel. There are many available speech recognition systems for doctors. Their effectiveness in languages such as Polish should be verified. In connection with our project in this field, we decided to check how well the popular speech recognition systems work, employing models trained for the general Polish language....
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An automated, low-latency environment for studying the neural basis of behavior in freely moving rats
PublicationBackground Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. Brain activity varies at sub-second time scales, but behavioral measures are usually coarse (often consisting of only binary trial outcomes). Results To overcome this mismatch, we developed the Rat Interactive Foraging Facility (RIFF): a programmable interactive arena for freely moving rats with multiple feeding...
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Applying the Lombard Effect to Speech-in-Noise Communication
PublicationThis study explored how the Lombard effect, a natural or artificial increase in speech loudness in noisy environments, can improve speech-in-noise communication. This study consisted of several experiments that measured the impact of different types of noise on synthesizing the Lombard effect. The main steps were as follows: first, a dataset of speech samples with and without the Lombard effect was collected in a controlled setting;...
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Autoencoder application for anomaly detection in power consumption of lighting systems
PublicationDetecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning...
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Automatic audio signal mixing system based on one-dimensional Wave-U-Net autoencoders
PublicationThe purpose of this dissertation is to develop an automatic song mixing system that is capable of automatically mixing a song with good quality in any music genre. This work recalls first the audio signal processing methods used in audio mixing, and it describes selected methods for automatic audio mixing. Then, a novel architecture built based on one-dimensional Wave-U-Net autoencoders is proposed for automatic music mixing. Models...
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Bimodal Emotion Recognition Based on Vocal and Facial Features
PublicationEmotion recognition is a crucial aspect of human communication, with applications in fields such as psychology, education, and healthcare. Identifying emotions accurately is challenging, as people use a variety of signals to express and perceive emotions. In this study, we address the problem of multimodal emotion recognition using both audio and video signals, to develop a robust and reliable system that can recognize emotions...
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Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice
PublicationThe vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron,...