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    Wyświetlanie 1-6 z 6
    Tytuł:
    Diagnostics of synchronous motor based on sound recognition with application of Linear Predictive Cepstrum Coefficients and fuzzy classifier
    Autorzy:
    Głowacz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/92999.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    sound recognition
    processing
    classification
    diagnostics
    fuzzy classifier
    Opis:
    This document provides the concept of investigations of acoustic signals of imminent failure conditions of synchronous motor. Measurements were made by recorder OLYMPUS WS-200S. Sound recognition software has been implemented. Algorithms of signal processing and analysis have been used. The system is based on the LPCC algorithm and fuzzy classifier with triangular membership function. Results confirm the correct operation of the system of sound recognition of synchronous motor.
    Źródło:
    Studia Informatica : systems and information technology; 2009, 2(13); 63-72
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    k-centroids clustering for asymmetric dissimilarities
    Autorzy:
    Olszewski, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/206422.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    k-centroids clustering
    asymmetric dissimilarity
    sound recognition
    heart rhythm recognition
    feature extraction
    Opis:
    In this paper, an asymmetric version of the kcentroids clustering algorithm is proposed. The asymmetry arises from the use of the asymmetric dissimilarities in the k-centroids algorithm. Application of the asymmetric measures of dissimilarity is motivated by the basic nature of the k-centroids algorithm, which uses dissimilarities in the asymmetric manner. It finds the minimal dissimilarity between an object being currently allocated, and one of the clusters centroids. Clusters centroids are treated as the dominant points governing the asymmetric relationships in the entire cluster analysis. The results of the experimental study on real and simulated data have shown the superiority of the asymmetric dissimilarities employed for the k-centroids method over their symmetric counterparts.
    Źródło:
    Control and Cybernetics; 2011, 40, 2; 554-574
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Diagnostyka silnika synchronicznego oparta na rozpoznawaniu dźwięku z zastosowaniem LPCC i GSDM
    Diagnostics of a synchronous motor based on sound recognition with application of LPCC and GSDM
    Autorzy:
    Głowacz, A.
    Głowacz, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/156930.pdf
    Data publikacji:
    2010
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    silnik synchroniczny
    rozpoznawanie dźwięku
    LPCC
    GSDM
    diagnostyka
    synchronous motor
    sound recognition
    LPCC (Linear Predictive Cepstrum Coefficients)
    GSDM (Genetic Sparse Distributed Memory)
    diagnostics
    Opis:
    Zaprezentowano koncepcję badania sygnałów akustycznych stanów przedawaryjnych silnika synchronicznego. Oprogramowanie do rozpoznawania dźwięku zostało zaimplementowane. Algorytmy przetwarzania i analizy sygnałów akustycznych zostały zastosowane. System jest oparty na algorytmie LPCC (Współczynniki cepstralne liniowego kodowania) i GSDM (Genetyczna rozrzedzona pamięć rozproszona). Badania zostały przeprowadzone dla sygnałów akustycznych stanów przedawaryjnych. Zmiany w sygnale akustycznym spowodowane były przez zwarcia i przerwy w obwodzie stojana. Analiza wyników pokazuje wrażliwość metody opartej na LPCC i GSDM w zależności od danych wejściowych. Wyniki badań potwierdzają poprawne działanie systemu rozpoznawania dźwięku silnika synchronicznego.
    In recent years the methods of sound recognition have been de-veloped. Hence, there is an idea to use them in case of machines. The paper describes the concept of investigations of acoustic signals of synchronous motor imminent failure conditions. Measurements were taken with a recorder OLYMPUS WS-200S. Sound recognition software was implemented. Algorithms of signal processing and analysis were used. The system is based on the LPCC (Linear Predictive Cepstrum Coefficients) algorithm and GSDM (Genetic Sparse Distributed Memory). Investigations were carried out for acoustic signals of imminent failure conditions. The following plan of investigations of a synchronous motor acoustic signal was proposed: recording of audio track, sound track division, sampling, quantization, normalization, filtration, windowing, feature extraction, classification (Fig. 2). Figs. 3, 4, 5 and 6 show changes of the LPCC values for four types of the categories recognized. Changes in the acoustic signal were caused by short circuit and broken coils in the stator circuit. The sound recognition efficiency depending on the acoustic signal and the sample length is presented in Fig. 8. The sound recognition system was built for a synchronous motor. There were used 39 band-pass filters in investigations. Analysis of the results shows the sensitivity of the method based on LPCC and GSDM, depending on the input data. The results confirm correct operation of the synchronous motor sound recognition system. These studies can be used for diagnostics based on acoustic emission in electrical, mechanical, hydraulic and pneumatic machines.
    Źródło:
    Pomiary Automatyka Kontrola; 2010, R. 56, nr 5, 5; 479-482
    0032-4140
    Pojawia się w:
    Pomiary Automatyka Kontrola
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Sentence Recognition in the Presence of Competing Speech Messages Presented in Audiometric Booths with Reverberation Times of 0.4 and 0.6 Seconds
    Autorzy:
    Abouchacra, K. S.
    Koehnke, J.
    Besing, J.
    Letowski, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/177482.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sound field testing
    reverberation
    speech recognition
    Opis:
    This study examined whether differences in reverberation time (RT) between typical sound field test rooms used in audiology clinics have an effect on speech recognition in multi-talker environments. Separate groups of participants listened to target speech sentences presented simultaneously with 0-to-3 competing sentences through four spatially-separated loudspeakers in two sound field test rooms having RT = 0:6 sec (Site 1: N = 16) and RT = 0:4 sec (Site 2: N = 12). Speech recognition scores (SRSs) for the Synchronized Sentence Set (S3) test and subjective estimates of perceived task difficulty were recorded. Obtained results indicate that the change in room RT from 0.4 to 0.6 sec did not significantly influence SRSs in quiet or in the presence of one competing sentence. However, this small change in RT affected SRSs when 2 and 3 competing sentences were present, resulting in mean SRSs that were about 8–10% better in the room with RT = 0:4 sec. Perceived task difficulty ratings increased as the complexity of the task increased, with average ratings similar across test sites for each level of sentence competition. These results suggest that site-specific normative data must be collected for sound field rooms if clinicians would like to use two or more directional speech maskers during routine sound field testing.
    Źródło:
    Archives of Acoustics; 2011, 36, 1; 3-14
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Automatic disordered sound repetition recognition in continuous speech using CWT and kohonen network
    Autorzy:
    Codello, I.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Kobus, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/106192.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
    Tematy:
    speech recognition
    speech disorders
    sound repetition
    Continuous Wavelet Transform
    WaveBlaster
    Opis:
    Automatic disorders recognition in speech can be very helpful for a therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales. Using the silence finding algorithm, only speech fragments are automatically found and cut. Each cut fragment is converted into a fixed-length vector and passed into the Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. Most of the analysis was performer and the results were obtained using the authors’ program WaveBlaster. We use the STATISTICA package for finding the best perceptron which was then imported back into WaveBlaster and used for automatic blockades finding. The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
    Źródło:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2012, 12, 2; 39-48
    1732-1360
    2083-3628
    Pojawia się w:
    Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Disordered sound repetition recognition in continuous speech using CWT and Kohonen network
    Autorzy:
    Codello, I.
    Kuniszyk-Jóźkowiak, W.
    Smołka, E.
    Kobus, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/333359.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    sieć Kohonena
    zaburzenia automatycznego rozpoznawania mowy
    ciągła transformata falkowa
    skala Barka
    powtarzanie dźwięku
    Kohonen network
    automatic disorders speech recognition
    waveblaster
    CWT
    continuous wavelet transform (CWT)
    Bark scale
    sound repetition
    Opis:
    Automatic disorders recognition in speech can be very helpful for therapist while monitoring therapy progress of patients with disordered speech. This article is focused on sound repetitions. The signal is analyzed using Continuous Wavelet Transform with 16 bark scales, the result is divided into vectors and passed into Kohonen network. Finally, the Kohonen winning neuron result is put on the 3-layer perceptron. The recognition ratio was increased by about 20% by adding a modification into the Kohonen network training process as well as into CWT computation algorithm. All the analysis was performed and the results were obtained using the authors' program ”WaveBlaster“, The problem presented in this article is a part of our research work aimed at creating an automatic disordered speech recognition system.
    Źródło:
    Journal of Medical Informatics & Technologies; 2011, 17; 123-130
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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