Filters
total: 188
filtered: 79
Chosen catalog filters
Search results for: signal features
-
The Multiplatform Environment for Simulation and Features Estimation of Mixed-Signal Devices
PublicationThe use of simulation laboratories is gaining popularity in thedomains of engineering programs. However, the experience in teaching showsthat the simulation itself is not very effective in didactic processes. Teachingprocesses in thefield of specialist subjects, designed for students of technicaluniversities, should be based on direct operations performed by the student onreal devices. At the same time, at the later stages of didactic...
-
Distributed Detection of Selected Features in Data Streams Using Grid-class Systems
PublicationThis chapter describes basic methodology of distributed digital signal processing. A choice of distributed methods of detection of selected features in data streams using grid-class systems is discussed. Problems related to distribution of data for processing are addressed. A mitigating method for data distribution and result merging is described.
-
Sleep Apnea Detection by Means of Analyzing Electrocardiographic Signal
PublicationObstructive sleep apnea (OSA) is a condition of cyclic, periodic ob-struction (stenosis) of the upper respiratory tract. OSA could be associated with serious cardiovascular problems, such as hypertension, arrhythmias, hearth failure or peripheral vascular disease. Understanding the way of connection between OSA and cardiovascular diseases is important to choose proper treatment strategy. In this paper, we present a method for integrated...
-
Efficient signal processing in spectroscopic optical coherence tomography
PublicationSpectroscopic optical coherence tomography (SOCT) is an extension of a standard OCT technique, which allows to obtain depth-resolved, spectroscopic information on the examined sample. It can be used as a source of additional contrast in OCT images e.g. by encoding certain features of the light spectrum into the hue of the image pixels. However, SOCT require computation of time-frequency distributions of each OCT A-scan, what is...
-
Improving the quality of speech in the conditions of noise and interference
PublicationThe aim of the work is to present a method of intelligent modification of the speech signal with speech features expressed in noise, based on the Lombard effect. The recordings utilized sets of words and sentences as well as disturbing signals, i.e., pink noise and the so-called babble speech. Noise signal, calibrated to various levels at the speaker's ears, was played over two loudspeakers located 2 m away from the speaker. In...
-
A study on of music features derived from audio recordings examples – a quantitative analysis
PublicationThe paper presents a comparative study of music features derived from audio recordings, i.e. the same music pieces but representing different music genres, excerpts performed by different musicians, and songs performed by a musician, whose style evolved over time. Firstly, the origin and the background of the division of music genres were shortly presented. Then, several objective parameters of an audio signal were recalled that...
-
seafloor characterisation combined approach using multibeam sonar echo signal processing and image analysis
PublicationThe authors propose the approach to seafloor characterisation which relies on the combined, concurrent use of two different techniques: (i) multibeam sonar image analysis and (ii) multibeam seabed echoes processing. The first technique is based on constructing the grey-level sonar images of the seabed extracted from the echoes received in the consecutive soundings. Then, the set of parameters describing the local region of sonar...
-
Automatic labeling of traffic sound recordings using autoencoder-derived features
PublicationAn approach to detection of events occurring in road traffic using autoencoders is presented. Extensions of existing algorithms of acoustic road events detection employing Mel Frequency Cepstral Coefficients combined with classifiers based on k nearest neighbors, Support Vector Machines, and random forests are used. In our research, the acoustic signal gathered from the microphone placed near the road is split into frames and converted...
-
Improvement of glass break acoustic signal detection via application of wavelet packet decomposition
PublicationThe main subject of the authors' research are non-contact methods of glass break detection based on analysis of the acoustic signal generated during the event. This problem has essential meaning for modern cost- effective alarm systems, particularly those installed into big buildings. The main difficulties of the matter are: transient character of the signal, great number of similar sounds (false signals, mainly accidental glass...
-
Stress Detection of Children With ASD Using Physiological Signals
PublicationThis paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot inter- vention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect...
-
Online Sound Restoration for Digital Library Applications
PublicationA system for sound restoration was conceived and engineered having the following features: no special sound restoration software is needed to perform audio restoration by the user, the process of restoration employs automatic reduction of noise, wow and impulse distortions performed in the online mode, no skills in digital signal processing from the user are needed. The principles of the created system and its features as well...
-
Semi-adaptive feedback active control of MRI noise
PublicationA feedback controller is proposed for cancellation of magnetic resonance imaging (MRI) noise. The design of the controller takes into account specific features of the MRI noise signal. Simulation results show that a considerable rejection rate of the MRI noise can be obtained.
-
EVALUATION OF LIQUID-GAS FLOW IN PIPELINE USING GAMMA-RAY ABSORPTION TECHNIQUE AND ADVANCED SIGNAL PROCESSING
PublicationLiquid-gas flows in pipelines appear in many industrial processes, e.g. in the nuclear, mining, and oil industry. The gamma-absorption technique is one of the methods that can be successfully applied to study such flows. This paper presents the use of thegamma-absorption method to determine the water-air flow parameters in a horizontal pipeline. Three flow types were studied in this work: plug, transitional plug-bubble,...
-
In uence of Low-Level Features Extracted from Rhythmic and Harmonic Sections on Music Genre Classi cation
PublicationWe present a comprehensive evaluation of the infuence of 'harmonic' and rhythmic sections contained in an audio file on automatic music genre classi cation. The study is performed using the ISMIS database composed of music files, which are represented by vectors of acoustic parameters describing low-level music features. Non-negative Matrix Factorization serves for blind separation of instrument components. Rhythmic components...
-
Investigating Feature Spaces for Isolated Word Recognition
PublicationThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
-
Investigation of the temperature modulation parameters on semiconductor gas sensor response
PublicationIn this work we present the results of the investigation of the sensing properties of semiconductor gas sensors with a sinusoidally modulated temperature in the presence of synthetic air (SA) and three volatile air pollutants, i.e. NH3, NO2 and SO2. The measurements were performed for different average sensor heater temperatures and the amplitude of the modulation signal. In addition, the extraction of features from the sensor...
-
Application of ANN and PCA to two-phase flow evaluation using radioisotopes
PublicationIn the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and...
-
Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
-
FFT analysis of temperature modulated semiconductor gas sensor response for the prediction of ammonia concentration under humidity interference
PublicationThe increasing environmental contamination forces the need to design reliable devices for detecting of the volatile compounds present in the air. For this purpose semiconductor gas sensors, which have been widely used for years, are often utilized. Although they have many advantages such as low price and quite long life time, they still lack of long term stability and selectivity. Namely, environmental conditions have significant...
-
Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion
PublicationA rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion....
-
Methodology and technology for the polymodal allophonic speech transcription
PublicationA method for automatic audiovisual transcription of speech employing: acoustic and visual speech representations is developed. It adopts a combining of audio and visual modalities, which provide a synergy effect in terms of speech recognition accuracy. To establish a robust solution, basic research concerning the relation between the allophonic variation of speech, i.e. the changes in the articulatory setting of speech organs for...
-
Methodology and technology for the polymodal allophonic speech transcription
PublicationA method for automatic audiovisual transcription of speech employing: acoustic, electromagnetical articulography and visual speech representations is developed. It adopts a combining of audio and visual modalities, which provide a synergy effect in terms of speech recognition accuracy. To establish a robust solution, basic research concerning the relation between the allophonic variation of speech, i.e., the changes in the articulatory...
-
Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublicationThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
-
Investigating Feature Spaces for Isolated Word Recognition
PublicationMuch attention is given by researchers to the speech processing task in automatic speech recognition (ASR) over the past decades. The study addresses the issue related to the investigation of the appropriateness of a two-dimensional representation of speech feature spaces for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and timefrequency signal representation...
-
Comparative analysis of various transformation techniques for voiceless consonants modeling
PublicationIn this paper, a comparison of various transformation techniques, namely Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete Walsh Hadamard Transform (DWHT) are performed in the context of their application to voiceless consonant modeling. Speech features based on these transformation techniques are extracted. These features are mean and derivative values of cepstrum coefficients, derived from each transformation....
-
Local atomic order in low Pt-content nanocatalysts investigated in situ by XAS
PublicationThe unique features of X-ray absorption spectroscopy allow investigations of nanosized catalysts for fuel cells under working conditions. We present the results of an experiment carried out on a low Pt content electrocatalyst supported by a mesoporous heteropolyacid salt and used at the cathode of a proton exchange membrane fuel cell (PEMFC). The analysis of the EXAFS signal at the Pt L3-edge indicates that upon operating the fuel...
-
Photoluminescence as a probe of phosphorene properties
PublicationHere, we provide a detailed evaluation of photoluminescence (PL) as a comprehensive tool for phosphorene characterization with the emphasis on a prominent quantitative role of PL in providing fingerprint-like features due to its extreme sensitivity to the band structure details, anisotropy, disorder, external fields, etc. Factors such as number of layers, dimensionality, structural and chemical disorder, and environmental factors...
-
Analysis of the harmonic structure of the vowel /a/ taking into account the age and gender of the speaker
PublicationSound waves are disturbances propagating through an elastic medium that, upon reaching the ear, elicit auditory sensations. Sounds generated by the surroundings can be captured by a transducer (microphone), which transforms them into an electrical signal. The signal from the microphone is then transmitted to a computer, where software allows for the extraction and analysis of individual tones. This process enables the description...
-
A family of new generation miniaturized impedance analyzers for technical object diagnostics
PublicationThe paper presents the family of three analyzers allowing to measure impedance in range of 10 ohm<|Zx|<10 Gohm in a wide frequency range from 10 mHz up to 100 kHz. The most important features of the analyzers family are: miniaturization, low power consumption, low production cost, telemetric controlling and the use of impedance measurement method based on digital signal processing DSP. The miniaturization and other above mentioned...
-
Personal adaptive tuning of mobile computer audio
PublicationAn integrated methodology for enhancing audio quality in mobile computers is presented. The key features are adaptation of the characteristics of the acoustic track to the changing conditions and to the user's individual preferences. Original signal processing algorithms are introduced, which concern: linearization of frequency response, dialogue intelligibility enhancement and dynamics processing tuned up to the user's preferences....
-
AUTOMATYCZNA KLASYFIKACJA MOWY PATOLOGICZNEJ
PublicationAplikacja przedstawiona w niniejszym rozdziale służy do automatycznego wykrywania mowy patologicznej na podstawie bazy nagrań. W pierwszej kolejności przedstawiono założenia leżące u podstaw przeprowadzonych badan wraz z wyborem bazy mowy patologicznej. Zaprezentowano również zastosowane algorytmy oraz cechy sygnału mowy, które pozwalają odróżnić mowę niezaburzoną od mowy patologicznej. Wytrenowane sieci neuronowe zostały następnie...
-
Characterization of Defects Inside the Cable Dielectric With Partial Discharge Modeling
PublicationThe continuous monitoring of power system devices is an important step toward keeping such capital assets safe. Partial discharge (PD)-based measurement tools provide a reliable and accurate condition assessment of power system insulations. It is very common that voids or cavities exist in every solid dielectric insulation medium. In this article, different voids are modeled and analyzed using an advanced finite element (FE)-based...
-
Classification of Music Genres Based on Music Separation into Harmonic and Drum Components . Klasyfikacja gatunków muzycznych wykorzystująca separację instrumentów muzycznych
PublicationThis article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector...
-
Adaptive Personal Tuning of Sound in Mobile Computers
PublicationAn integrated methodology for enhancing audio quality in mobile computers is presented. The key features are adaptation of the characteristics of their acoustic track to changing acoustic conditions of the environment and to users’ individual preferences. Signal processing algorithms are introduced that concern: linearization of frequency response, dialogue intelligibility enhancement, and dynamics processing tuned up to the users’...
-
Evaluation of sound event detection, classification and localization in the presence of background noise for acoustic surveillance of hazardous situations
PublicationAn evaluation of the sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for separating foreground events from the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the classifier...
-
Assessment of organic coating degradation via local impedance imaging
PublicationThe paper presents a new approach to organic coating condition evaluation at micrometer scale using localized impedance measurements. It is based on atomic force microscopy (AFM) in contact mode. Impedance is measured between conductive AFM tip and metal substrate covered with organic coating. A single-frequency voltage perturbation signal is applied between the electrodes and current response signal is registered. As the tip is...
-
Optical Coherence Tomography for nanoparticles quantitative characterization
PublicationThe unique features of nanocomposite materials depend on the type and size of nanoparticles, as well as their placement in the composite matrices. Therefore the nanocomposites manufacturing process requires inline control over certain parameters of nanoparticles such as dispersion and concentration. Keeping track of nanoparticles parameters inside a matrix is currently a difficult task due to lack of a fast, reliable and cost effective...
-
Selection of Features for Multimodal Vocalic Segments Classification
PublicationEnglish speech recognition experiments are presented employing both: audio signal and Facial Motion Capture (FMC) recordings. The principal aim of the study was to evaluate the influence of feature vector dimension reduction for the accuracy of vocalic segments classification employing neural networks. Several parameter reduction strategies were adopted, namely: Extremely Randomized Trees, Principal Component Analysis and Recursive...
-
Cichy sonar - stan aktualny i perspektywy
PublicationW zastosowaniach militarnych często istnieje potrzeba prowadzenia obserwacji w sposób skryty za pomocą urządzeń emitujących sygnały trudne do przechwycenia przez przeciwnika. Rozwiązania stosowane w cichych radarach stanowiły punkt wyjścia do opracowania cichego sonaru. Prace nad projektem rozpoczęto na Politechnice Gdańskiej w roku 2010 w ramach Grantu NCBiR i są one dalej kontynuowane. W celu zachowania w cichym sonarze warunków...
-
Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublicationAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
-
Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublicationAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
-
Mining Knowledge of Respiratory Rate Quantification and Abnormal Pattern Prediction
PublicationThe described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis...
-
APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS
PublicationAbnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregu- larity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated...
-
Detection, classification and localization of acoustic events in the presence of background noise for acoustic surveillance of hazardous situations
PublicationEvaluation of sound event detection, classification and localization of hazardous acoustic events in the presence of background noise of different types and changing intensities is presented. The methods for discerning between the events being in focus and the acoustic background are introduced. The classifier, based on a Support Vector Machine algorithm, is described. The set of features and samples used for the training of the...
-
Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublicationMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
-
Techniques of acquiring additional features of the responses of individual gas sensors
PublicationGas sensors usually exhibit lack of selectivity, require fre quent calibration, exhibit drift of the response and a lot of factors, such as humidity or ambient temperature, influen ce their performance. Different approaches can be used to overcome this shortcomings. Building arrays of different sensors and usage of pattern recognition methods to analyze responses of elements...
-
Buzz-based recognition of the honeybee colony circadian rhythm
PublicationHoneybees are one of the highly valued pollinators. Their work as individuals is appreciated for crops pollination and honey production. It is believed that work of an entire bee colony is intense and almost continuous. The goal of the work presented in this paper is identification of bees circadian rhythm with a use of sound-based analysis. In our research as a source of information on bee colony we use their buzz that have been...
-
Impact of Tensile and Compressive Stress on Classical and Acoustic Barkhausen Effects in Grain-Oriented Electrical Steel
PublicationIn this paper, we present the results of the investigation of impact of tensile and compressive stress on the classical Barkhausen effect, magnetoacoustic emission (MAE) signal properties, and B(H) hysteresis loops for grain-oriented (GO) electrical steel. Samples have been glued to a nonmagnetic steel bar and stressed within elastic range (±800 μdef) by means of four-point bending method. The samples were cut out in two directions...
-
A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces
PublicationIn this research, a study of cross-linguistic speech emotion recognition is performed. For this purpose, emotional data of different languages (English, Lithuanian, German, Spanish, Serbian, and Polish) are collected, resulting in a cross-linguistic speech emotion dataset with the size of more than 10.000 emotional utterances. Despite the bi-modal character of the databases gathered, our focus is on the acoustic representation...
-
Testing OFDM Transmission Schemes for Shallow Water Acoustic Communications
PublicationLarge variability of communication properties of underwater acoustic channels, and especially strongly varying instantaneous conditions in shallow waters, is a tough challenge for the designers of underwater acoustics communication (UAC system. There is a need for developing adaptive signaling schemes that would dynamically optimize signal parameters in both physical and link layers of communication protocols. The orthogonal frequency...