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    Wyświetlanie 1-7 z 7
    Tytuł:
    Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition
    Autorzy:
    Majeed, S. A.
    Husain, H.
    Samad, S. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/177326.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech recognition
    feature extraction
    phase autocorrelation
    wavelet transform
    Opis:
    In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions.
    Źródło:
    Archives of Acoustics; 2015, 40, 1; 25-31
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
    Autorzy:
    Wang, Can
    Peng, Jianxin
    Zhang, Xiaowen
    Powiązania:
    https://bibliotekanauki.pl/articles/176601.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    acoustical analysis
    feature extraction
    support vector machine
    snoring sound
    Opis:
    Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
    Źródło:
    Archives of Acoustics; 2020, 45, 1; 141-151
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Fast Method of Feature Extraction for Lowering Vehicle Pass-By Noise Based on Nonnegative Tucker3 Decomposition
    Autorzy:
    Wang, H.
    Cheng, G.
    Deng, G.
    Li, X.
    Li, H.
    Huang, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/177883.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    vehicle pass-by noise
    NTD
    feature extraction
    sound pressure level
    Opis:
    Usually, the judgement of one type fault of vehicle pass-by noise is difficult for engineers, especially when some significant features are disturbed by other interference noise, such as the squealing noise is almost simultaneous with the whistle in the exhaust system. In order to cope with this problem, a new method, with the antinoise ability of the algorithm on the condition by which the features are entangled, is developed to extract clear features for the fault analysis. In the proposed method, the nonnegative Tucker3 decomposition (NTD) with fast updating algorithm, signed as NTD_FUP, can find out the natural frequency of the parts/components from the exhaust system. Not only does the NTD_FUP extract clear features from the confused noise, but also it is superior to the traditional methods in practice. Then, an aluminium-foil alloy material, which is used for the heat shield for its lower noise radiation, replaces the aluminium alloy alone. Extensive experiments show that the sound pressure level of the vehicle pass-by noise is reduced 0.9 dB(A) by the improved heat shield, which is also considered as a more lightweight design for the exhaust system of an automobile.
    Źródło:
    Archives of Acoustics; 2017, 42, 4; 619-629
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    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:
    Hybridisation of Mel Frequency Cepstral Coefficient and Higher Order Spectral Features for Musical Instruments Classification
    Autorzy:
    Bhalke, D. G.
    Rama Rao, C. B.
    Bormane, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/176497.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    feature extraction
    MFCC
    HOS
    bispectrum
    bicoherence
    non-linearity
    non-Gaussianity
    CPNN
    zero crossing rate (ZCR)
    Opis:
    This paper presents the classification of musical instruments using Mel Frequency Cepstral Coefficients (MFCC) and Higher Order Spectral features. MFCC, cepstral, temporal, spectral, and timbral features have been widely used in the task of musical instrument classification. As music sound signal is generated using non-linear dynamics, non-linearity and non-Gaussianity of the musical instruments are important features which have not been considered in the past. In this paper, hybridisation of MFCC and Higher Order Spectral (HOS) based features have been used in the task of musical instrument classification. HOS-based features have been used to provide instrument specific information such as non-Gaussianity and non-linearity of the musical instruments. The extracted features have been presented to Counter Propagation Neural Network (CPNN) to identify the instruments and their family. For experimentation, isolated sounds of 19 musical instruments have been used from McGill University Master Sample (MUMS) sound database. The proposed features show the significant improvement in the classification accuracy of the system.
    Źródło:
    Archives of Acoustics; 2016, 41, 3; 427-436
    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:
    Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features
    Autorzy:
    Bhalke, D. G.
    Rajesh, B.
    Bormane, D. S.
    Powiązania:
    https://bibliotekanauki.pl/articles/177599.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    feature extraction
    Timbral features
    MFCC
    Mel Frequency Cepstral Coefficient
    FrFT
    fractional Fourier transform
    Fractional MFCC
    Tamil Carnatic music
    Opis:
    This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 213-222
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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