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Search results for: LOMBARD EFFECT, SPEECH DETECTION, NOISE SIGNAL, SELF-SIMILARITY MATRIX, CONVOLUTIONAL NEURAL NETWORK
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Difference in Perceived Speech Signal Quality Assessment Among Monolingual and Bilingual Teenage Students
PublicationThe user perceived quality is a mixture of factors, including the background of an individual. The process of auditory perception is discussed in a wide variety of fields, ranging from engineering to medicine. Many studies examine the difference between musicians and non-musicians. Since musical training develops musical hearing and other various auditory capabilities, similar enhancements should be observable in case of bilingual...
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An idea of an approach to self-testing of mixed signal systems based on a quadratic function stimulation
PublicationA new approach to self-testing of the analog parts of mixed-signal electronic systems controlled by microcontrollers equipped with an ADC and a DAC is presented. It is based on a BIST and a new fault diagnosis method. A novelty is the use of the DAC as a component of the BIST, allowing to generate a stimulating signal with a quadratic function shape. It contributes to a better extraction of information about the state of the circuit...
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Speech codec enhancements utilizing time compression and perceptual coding
PublicationA method for encoding wideband speech signal employing standardized narrowband speech codecs is presented as well as experimental results concerning detection of tonal spectral components. The speech signal sampled with a higher sampling rate than it is suitable for narrowband coding algorithm is compressed in order to decrease the amount of samples. Next, the time-compressed representation of a signal is encoded using a narrowband...
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Identification of Optocoupler Devices with RTS Noise
PublicationThe results of noise measurements in low frequency range for CNY 17 type optocouplers are presented. The research were carried out on devices with different values of Current Transfer Ratio (CTR). The methods for identification of Random Telegraph Signal (RTS) in noise signal of optocouplers were proposed. It was found that the Noise Scattering Pattern method (NSP method) enables to identify RTS noise as non-Gaussian component...
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Butyrylcholinesterase signal sequence self-aggregates and enhances amyloid fibril formation in vitro
PublicationAlzheimer’s disease (AD) pathogenesis has been attributed to extracellular aggregates of amyloid β (Aβ) plaques and neurofibrillary tangles in the human brain. It has been reported that butyrylcholinesterase (BChE) also accumulates in the brain Aβ plaques in AD. We have previously found that the BChE substitution in 5′UTR caused an in-frame N-terminal extension of 41 amino acids of the BChE signal peptide. The resultant variant...
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Change Detection in Signals 2023
e-Learning CoursesChange Detection in Signals - files for students.
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Multichannel self-optimizing narrowband interference canceller
PublicationThe problem of cancellation of a nonstationary sinusoidal interference, acting at the output of an unknown multivariable linear stable plant, is considered. No reference signal is assumed to be available. The proposed feedback controller is a nontrivial extension of the SONIC (self-optimizing narrowband interference canceller) algorithm, developed earlier for single-input, single-output plants. The algorithm consists of two loops:...
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An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes
PublicationA problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and...
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Electrical and noise responses of carbon nanotube networks enhanced by UV light for detection of organic gases (ethanol, acetone)
Open Research DataCarbon nanotube networks of different optical transparencies were investigated via resistance and 1/f noise measurements for detection of ethanol and acetone. The sensor resistive and noise responses were collected for dark and UV-assisted conditions, revealing the improvement in sensor sensitivity and limit of detection after applying UV light (275...
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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...
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Speech Analytics Based on Machine Learning
PublicationIn this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
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Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network
PublicationThe contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed...
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A low complexity double-talk detector based on the signal envelope
PublicationA new algorithm for double-talk detection, intended for use in the acoustic echo canceller for voice communication applications, is proposed. The communication system developed by the authors required the use of a double-talk detection algorithm with low complexity and good accuracy. The authors propose an approach to doubletalk detection based on the signal envelopes. For each of three signals: the far-end speech, the microphone...
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Toward Intelligent Vehicle Intrusion Detection Using the Neural Knowledge DNA
PublicationIn this paper, we propose a novel intrusion detection approach using past driving experience and the neural knowledge DNA for in-vehicle information system security. The neural knowledge DNA is a novel knowledge representation method designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing systems. We examine our approach for classifying malicious vehicle control commands...
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Artificial Neural Network in Forecasting the Churn Phenomena Among Costumers of IT and Power Supply Services
PublicationThis paper presents an attempt to use an artificial neural network to investigate the churn phenomenon among the customers of a telecommunications operator. An attempt was made to create a data model based on the customer lifetime value (CLV) rather than on activity alone. A multilayered artificial neural network was used for the experiments. The results yielded a 99% successful identification rate for customers in no danger of...
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Detection of dialogue in movie soundtrack for speech intelligibility enhancement
PublicationA method for detecting dialogue in 5.1 movie soundtrack based on interchannel spectral disparity is presented. The front channel signals (left, right, center) are analyzed in the frequency domain. The selected partials in the center channel signal, which yield high disparity with left and right channels, are detected as dialogue. Subsequently, the dialogue frequency components are boosted to achieve increased dialogue intelligibility....
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Gas Detection Using Resistive Gas Sensors And Radial Basis Function Neural Networks
PublicationWe present a use of Radial Basis Function (RBF) neural networks and Fluctuation Enhanced Sensing (FES) method in gas detection system utilizing a prototype resistive WO3 gas sensing layer with gold nanoparticles. We investigated accuracy of gas detection for three different preprocessing methods: no preprocessing, Principal Component Analysis (PCA) and wavelet transformation. Low frequency noise voltage observed in resistive gas...
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Outlier detection method by using deep neural networks
PublicationDetecting outliers in the data set is quite important for building effective predictive models. Consistent prediction can not be made through models created with data sets containing outliers, or robust models can not be created. In such cases, it may be possible to exclude observations that are determined to be outlier from the data set, or to assign less weight to these points of observation than to other points of observation....
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POPRAWA OBIEKTYWNYCH WSKAŹNIKÓW JAKOŚCI MOWY W WARUNKACH HAŁASU
PublicationCelem pracy jest modyfikacja sygnału mowy, aby uzyskać zwiększenie poprawy obiektywnych wskaźników jakości mowy po zmiksowaniu sygnału użytecznego z szumem bądź z sygnałem zakłócającym. Wykonane modyfikacje sygnału bazują na cechach mowy lombardzkiej, a w szczególności na efekcie podniesienia częstotliwości podstawowej F0. Sesja nagraniowa obejmowała zestawy słów i zdań w języku polskim, nagrane w warunkach ciszy, jak również w...
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Elimination of impulsive disturbances from archive audio files – comparison of three noise pulse detection schemes
PublicationThe problem of elimination of impulsive disturbances (such as clicks, pops, ticks, crackles, and record scratches) from archive audio recordings is considered and solved using autoregressive modeling. Three classical noise pulse detection schemes are examined and compared: the approach based on open-loop multi-step-ahead signal prediction, the approach based on decision-feedback signal prediction, and the double threshold approach,...
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Andrzej Czyżewski prof. dr hab. inż.
PeopleProf. zw. dr hab. inż. Andrzej Czyżewski jest absolwentem Wydziału Elektroniki PG (studia magisterskie ukończył w 1982 r.). Pracę doktorską na temat związany z dźwiękiem cyfrowym obronił z wyróżnieniem na Wydziale Elektroniki PG w roku 1987. W 1992 r. przedstawił rozprawę habilitacyjną pt.: „Cyfrowe operacje na sygnałach fonicznych”. Jego kolokwium habilitacyjne zostało przyjęte jednomyślnie w czerwcu 1992 r. w Akademii Górniczo-Hutniczej...
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The effect of current signal filtering method on the value of cutting power while sawing wood
PublicationThe goal of this work was to investigate an effect of various signal pre-processings on the outline of the electrical power curve and its influence on the measured cutting force estimation. Two signal processing methods were selected for the needs of the experiment, including digital filter and wavelet transform. The filter used was Butterworth, 3rd order band-stop with the cut-out band from 45 Hz to 55 Hz. The second approach...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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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...
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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...
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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...
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Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals
PublicationIt is important from the economic point of view to detect damage early in the wind turbines before failures occur. For this purpose, a monitoring device was built that analyzes both acoustic signals acquired from the built-in non-contact acoustic intensity probe, as well as from the accelerometers, mounted on the internal devices in the nacelle. The signals collected in this way are used for long-term training of the autoencoder...
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EPILEPTIC BEHAVIOR WITH A DISTINGUISHED PREICTAL PERIOD IN A LARGE-SCALE NEURAL NETWORK MODEL
PublicationWe present a neural network model capable of reproducing focal epileptic behavior. An important property of our model is the distinguished preictal state. This novel feature may shed light on the pathologi-cal mechanisms of seizure generation and, in perspective, help develop new therapeutic strategies to manage refractory partial epilepsy.
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Geospatial Coverage and Signal Quality Measurements of Terrestrial DAB+ Network in Northern Poland
PublicationModern signal coverage maps are prepared based on industry-standard radio propagation models, which take into account a number of parameters, including: type of antenna, distance from the transmitter, type of terrain, etc. However, such simulations are prone to location-specific inaccuracies, and should be verified with in-situ measurements. This paper presents results of a field test of a terrestrial DAB+ (Digital Audio Broadcasting...
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Estimation of the excitation variances of speech and noise AR-models for enhanced speech coding
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Multichannel self-optimizing active noise control scheme
PublicationThe problem of cancellation of a nonstationary sinusoidal interference, acting at the output of an unknown multivariable linear stable plant, is considered. The proposed cancellation scheme is a nontrivial extension of the SONIC (self-optimizing narrowband interference canceller) algorithm, developed earlier for single-input, single-output plants. In the important benchmark case - for disturbances with randomwalk-type amplitude...
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NETWORK-COMPUTATION IN NEURAL SYSTEMS
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publicationconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation
PublicationAutomatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the...
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Theoretical and experimental investigation of low-noise optoelectronic system configurations for low-coherent optical signal detection
PublicationW artykule przedstawiono wyniki teoretycznej i eksperymentalnej analizy konfiguracji niskokoherencyjnego układu detekcji sygnałów optycznych. Zaprezentowano zaprojektowany układ detekcji optycznych sygnałów niskokoherencyjnych, który pozwala na pracę w konfiguracji zrównoważonej i niezrównoważonej, co umożliwia porównanie parametrów obu konfiguracji.
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Emotion Recognition from Physiological Channels Using Graph Neural Network
PublicationIn recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The...
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Magnetic field gradient as the most useful signal for detection of flaws using MFL technique
PublicationThe magnetic flux leakage (MFL) technique is extensively used for detection of flaws as well as for evaluation of their dimensions in ferromagnetic materials. However, proper analysis of the MFL signal is hindered by the MFL sensor velocity causing distortions of this signal. Traditionally measured components of the MFL signal are particularly sensitive to the scanning velocity. In this paper, an another signal – the gradient of...
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Playback detection using machine learning with spectrogram features approach
PublicationThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
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1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type
PublicationA network architecture that may be employed to sensing and recognition of a type of vehicle on the basis of audio recordings made in the proximity of a road is proposed in the paper. The analyzed road traffic consists of both passenger cars and heavier vehicles. Excerpts from recordings that do not contain vehicles passing sounds are also taken into account and marked as ones containing silence....
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Investigations of speech signal parameters with regard to articulation influences
PublicationW pracy zostało podjęte zagadnienie parametryzacji sygnału mowy w kontekście ekstrakcji cech biometrycznych. Analizowane parametry to parametry cepstralne (cepstrum liniowe i mel-cepstrum, czyli MFCC), parametry liniowej predykcji (LPC) oraz momenty widmowe i parametr F0. Zastosowano analize w krótkich stałych segmentach sygnału z zastosowaniem dużego zakładkowania, tzw. ''implicite segmentation''. Umożliwiło to zaobserwowanie...
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System of speech signal processing and visualisation for linguistic purposes
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Language Models in Speech Recognition
PublicationThis chapter describes language models used in speech recognition, It starts by indicating the role and the place of language models in speech recognition. Mesures used to compare language models follow. An overview of n-gram, syntactic, semantic, and neural models is given. It is accompanied by a list of popular software.
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublicationPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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Application of Complementary Signals in Built-In Self Testers for Mixed-Signal Embedded Electronic Systems
PublicationThis paper concerns the implementation of shape-designed complementary signals (CSs), matched to the frequency characteristic of the circuit under test, in built-in self testers (BISTs), dedicated to mixed-signal embedded electronic systems for testing their analog sections. The essence of the proposed method and solution of CS BIST is low-cost realization on the base of hardware and software resources of microcontrollers used...
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Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction
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Elimination of clicks from archive speech signals using sparse autoregressive modeling
PublicationThis paper presents a new approach to elimination of impulsivedisturbances from archive speech signals. The proposedsparse autoregressive (SAR) signal representation is given ina factorized form - the model is a cascade of the so-called formantfilter and pitch filter. Such a technique has been widelyused in code-excited linear prediction (CELP) systems, as itguarantees model stability. After detection of noise pulses usinglinear...
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Effect of illumination on noise of silicon solar cells
PublicationTransport and noise properties of silicon solar cells in darkness and under illumination have been studied. The measurements were carried out for both reverse and forward bias of the device. The changes in the sample behaviour are due to the variation of PN junction dynamic resistance and, also, the occurence of both 1/f and generation-recombination noise components
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Noise as stimulus signal for digital circuits noise immunity measurementand simulation
PublicationWyniki pomiarów podatności na zakłócenia układów cyfrowych istotnie zależą od parametrów sygnału stymulujacego. Przedstawiono metodę pomiaru i symulacji dynamicznej odporności na zakłócenia z zastosowaniem sygnału szumu białego o różnej szerokości pasma częstotliwości. Modele symulacyjne dla sygnału pobudzającego (gaussowskiego szumu białego) zarówno szerokopasmowego jak i wąskopasmowego opracowano w środowisku programistycznym...
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ReFlexeNN - the Wearable EMG Interface with Neural Network Based Gesture Classification
PublicationThe electromyographic activity of muscles was measured using a wireless biofeedback device. The aim of the study was to examine the possibility of creating an automatic muscle tension classifier. Several measurement series were conducted and the participant performed simple physical exercises - forcing the muscle to increase its activity accordingly to the selected scale. A small wireless device was attached to the electrodes placed...
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A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System
PublicationMachine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved it would streamline the radiologists work. To deal with this complex three-dimensional...