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    Tytuł:
    A cough-based COVID-19 detection with gammatone and Mel-frequency cepstral coefficients
    Autorzy:
    Benmalek, Elmehdi
    El Mhamdi, Jamal
    Jilbab, Abdelilah
    Jbari, Atman
    Powiązania:
    https://bibliotekanauki.pl/articles/2203646.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    COVID-19
    cough recordings
    machine learning
    mel-frequency cepstral coefficients
    gammatone cepstral coefficients
    feature selection
    uczenie maszynowe
    współczynniki mel-cepstralne
    Opis:
    Many countries have adopted a public health approach that aims to address the particular challenges faced during the pandemic Coronavirus disease 2019 (COVID-19). Researchers mobilized to manage and limit the spread of the virus, and multiple artificial intelligence-based systems are designed to automatically detect the disease. Among these systems, voice-based ones since the virus have a major impact on voice production due to the respiratory system's dysfunction. In this paper, we investigate and analyze the effectiveness of cough analysis to accurately detect COVID-19. To do so, we distinguished positive COVID patients from healthy controls. After the gammatone cepstral coefficients (GTCC) and the Mel-frequency cepstral coefficients (MFCC) extraction, we have done the feature selection (FS) and classification with multiple machine learning algorithms. By combining all features and the 3-nearest neighbor (3NN) classifier, we achieved the highest classification results. The model is able to detect COVID-19 patients with accuracy and an f1-score above 98 percent. When applying FS, the higher accuracy and F1-score were achieved by the same model and the ReliefF algorithm, we lose 1 percent of accuracy by mapping only 12 features instead of the original 53.
    Źródło:
    Diagnostyka; 2023, 24, 2; art. no. 2023214
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech signal processing and analysis tool
    Narzędzie do przetwarzania i analizy sygnału mowy
    Autorzy:
    Mięsikowska, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/158001.pdf
    Data publikacji:
    2007
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    przetwarzanie sygnału mowy
    współczynniki cepstralne
    speech signal processing
    cepstral coefficients
    Opis:
    The project's objective is to create a tool intended for processing, analysis, and parameterizing human speech signal. The main aim is to obtain a speech signal image with some selected parameterization methods. The methods include use of 2D parameterization grid [1, 2] as well as cepstral coefficients CC [3]. Obtaining signal image as well as its further analysis without signal preprocessing is extremely difficult and the process doesn't guarantee desirable results. For this reason the tool is based on two main modules. The first one is intended for signal preprocessing, preparing it for further analysis. The other one provides signal parameterization methods. The tool was implemented in Java language.
    W pracy podjęto próbę stworzenia narzędzia umożliwiającego przetwarzanie, analizę i parametryzację sygnału mowy. Głównym celem jest pozyskanie obrazu sygnału mowy za pomocą wybranych metod parametryzacji. Wybrane metody parametryzacji sygnału mowy to parametryzacja za pomocą siatki dwuwymiarowej [1, 2] oraz współczynniki cepstralne [3]. Zobrazowanie sygnału oraz jego dalsza analiza bez operacji wstępnego przetworzenia sygnału jest procesem trudnym i nie zawsze przynosi pożądane rezultaty. Wobec tego narzędzie wyposażono w dwa zasadnicze moduły. Pierwszy moduł odpowiedzialny jest za wstępne przetworzenie sygnału, przygotowujące sygnał do dalszej analizy. Drugi moduł dostarcza metod parametryzacji sygnału mowy. Narzędzie zaimplementowano w języku Java.
    Źródło:
    Pomiary Automatyka Kontrola; 2007, R. 53, nr 12, 12; 43-45
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features
    Autorzy:
    Atmani, Youcef
    Rechak, Said
    Mesloub, Ammar
    Hemmouche, Larbi
    Powiązania:
    https://bibliotekanauki.pl/articles/177335.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    bearing faults
    Gaussian mixture models
    Mel frequency cepstral coefficients
    feature extraction
    diagnosis
    Opis:
    Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.
    Źródło:
    Archives of Acoustics; 2020, 45, 2; 283-295
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Visualization of stages of determining cepstral factors in speech recognition systems
    Autorzy:
    Proksa, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/333103.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    rozpoznawanie mowy
    LPCC
    MFCC
    wyizolowane słowo
    sygnały mowy
    speech recognition
    cepstral coefficients
    isolated word
    Opis:
    The article presents two methods of determination of cepstral parameters commonly applied in digital signal processing, in particular in speech recognition systems. The solutions presented are part of a project aimed at developing applications allowing to control the Windows operating system with voice and the use of MSAA (Microsoft Active Accessibility). The analysed voice signal has been visually presented at each of the crucial stages of developing cepstral coefficients.
    Źródło:
    Journal of Medical Informatics & Technologies; 2009, 13; 121-128
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Music Performers Classification by Using Multifractal Features : A Case Study
    Autorzy:
    Reljin, N.
    Pokrajac, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/177266.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    music classification
    multifractal analysis
    support vector machines
    cross-validation
    mel-frequency cepstral coefficients
    Opis:
    In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 223-233
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Marine Mammals Classification using Acoustic Binary Patterns
    Autorzy:
    Nadir, Maheen
    Adnan, Syed M.
    Aziz, Sumair
    Khan, Muhammad Umar
    Powiązania:
    https://bibliotekanauki.pl/articles/1953520.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    marine mammals
    1D Local Binary Patterns
    Mel frequency cepstral coefficients
    feature extraction
    passive acoustic monitoring
    Opis:
    Marine mammal identification and classification for passive acoustic monitoring remain a challenging task. Mainly the interspecific and intraspecific variations in calls within species and among different individuals of single species make it more challenging. Varieties of species along with geographical diversity induce more complications towards an accurate analysis of marine mammal classification using acoustic signatures. Prior methods for classification focused on spectral features which result in increasing bias for contour base classifiers in automatic detection algorithms. In this study, acoustic marine mammal classification is performed through the fusion of 1D Local Binary Pattern (1D-LBP) and Mel Frequency Cepstral Coefficient (MFCC) based features. Multi-class Support Vector Machines (SVM) classifier is employed to identify different classes of mammal sounds. Classification of six species named Tursiops truncatus, Delphinus delphis, Peponocephala electra, Grampus griseus, Stenella longirostris, and Stenella attenuate are targeted in this research. The proposed model achieved 90.4% accuracy on 70-30% training testing and 89.6% on 5-fold cross-validation experiments.
    Źródło:
    Archives of Acoustics; 2020, 45, 4; 721-731
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Determination of Input Parameters of the Neural Network Model, Intended for Phoneme Recognition of a Voice Signal in the Systems of Distance Learning
    Autorzy:
    Akhmetov, B.
    Tereykovsky, I.
    Doszhanova, A.
    Tereykovskaya, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/226378.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    neural networks
    phonemes
    recognition of a voice signal
    system of distance learning
    mel-cepstral coefficients
    spectral analysis
    Opis:
    The article is devoted to the problem of voice signals recognition means introduction in the system of distance learning. The results of the conducted research determine the prospects of neural network means of phoneme recognition. It is also shown that the main difficulties of creation of the neural network model, intended for recognition of phonemes in the system of distance learning, are connected with the uncertain duration of a phoneme-like element. Due to this reason for recognition of phonemes, it is impossible to use the most effective type of neural network model on the basis of a multilayered perceptron, at which the number of input parameters is a fixed value. To mitigate this shortcoming, the procedure, allowing to transform the non-stationary digitized voice signal to the fixed quantity of mel-cepstral coefficients, which are the basis for calculation of input parameters of the neural network model, is developed. In contrast to the known ones, the possibility of linear scaling of phoneme-like elements is available in the procedure. The number of computer experiments confirmed expediency of the fact that the use of the offered coding procedure of input parameters provides the acceptable accuracy of neural network recognition of phonemes under near-natural conditions of the distance learning system. Moreover, the prospects of further research in the field of development of neural network means of phoneme recognition of a voice signal in the system of distance learning is connected with an increase in admissible noise level. Besides, the adaptation of the offered procedure to various natural languages, as well as to other applied tasks, for instance, a problem of biometric authentication in the banking sector, is also of great interest.
    Źródło:
    International Journal of Electronics and Telecommunications; 2018, 64, 4; 425-432
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effect of Time-domain Windowing on Isolated Speech Recognition System Performance
    Autorzy:
    Ananthakrishna, Thalengala
    Anitha, H.
    Girisha, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/2055228.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    hidden Markov model
    HMM
    isolated speech recognition system
    ISR
    Kannada language
    mono-phone model
    Mel frequency cepstral coefficients
    MFCC
    Opis:
    Speech recognition system extract the textual data from the speech signal. The research in speech recognition domain is challenging due to the large variabilities involved with the speech signal. Variety of signal processing and machine learning techniques have been explored to achieve better recognition accuracy. Speech is highly non-stationary in nature and therefore analysis is carried out by considering short time-domain window or frame. In the speech recognition task, cepstral (Mel frequency cepstral coefficients (MFCC)) features are commonly used and are extracted for short time-frame. The effectiveness of features depend upon duration of the time-window chosen. The present study is aimed at investigation of optimal time-window duration for extraction of cepstral features in the context of speech recognition task. A speaker independent speech recognition system for the Kannada language has been considered for the analysis. In the current work, speech utterances of Kannada news corpus recorded from different speakers have been used to create speech database. The hidden Markov tool kit (HTK) has been used to implement the speech recognition system. The MFCC along with their first and second derivative coefficients are considered as feature vectors. Pronunciation dictionary required for the study has been built manually for mono-phone system. Experiments have been carried out and results have been analyzed for different time-window lengths. The overlapping Hamming window has been considered in this study. The best average word recognition accuracy of 61.58% has been obtained for a window length of 110 msec duration. This recognition accuracy is comparable with the similar work found in literature. The experiments have shown that best word recognition performance can be achieved by tuning the window length to its optimum value.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 1; 161--166
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys
    Autorzy:
    Valero, X.
    Alias, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/176616.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    acoustic signature
    environmental noise monitoring
    Gaussian mixture models
    hierarchical classification
    mel-frequency cepstral coefficients (MFCC)
    sound classification
    traffic noise
    vehicle pass-by
    Opis:
    This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.
    Źródło:
    Archives of Acoustics; 2012, 37, 4; 423-434
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A class of neuro-computational methods for assamese fricative classification
    Autorzy:
    Patgiri, C.
    Sarma, M.
    Sarma, K. K.
    Powiązania:
    https://bibliotekanauki.pl/articles/91763.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    neuro-computational classifier
    fricative phonemes
    Assamese language
    Recurrent Neural Network
    RNN
    neuro fuzzy classifier
    linear prediction cepstral coefficients
    LPCC
    self-organizing map
    SOM
    adaptive neuro-fuzzy inference system
    ANFIS
    klasyfikator neuronowy
    klasyfikator neuronowo rozmyty
    sieć Kohonena
    Opis:
    In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 1; 59-70
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-10 z 10

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