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    Wyświetlanie 1-2 z 2
    Tytuł:
    Enhancing Speech Recognition in Adverse Listening Environments: The Impact of Brief Musical Training on Older Adults
    Autorzy:
    Nandakumar, Akhila R
    Somashekara, Haralakatta Shivananjappa
    Kanagokar, Vibha
    Pitchaimuthu, Arivudai Nambi
    Powiązania:
    https://bibliotekanauki.pl/articles/31339763.pdf
    Data publikacji:
    2024
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    musical training
    carnatic music
    speech recognition in noise
    speech recognition in reverberation
    Opis:
    The present research investigated the effects of short-term musical training on speech recognition in adverse listening conditions in older adults. A total of 30 Kannada-speaking participants with no history of gross otologic, neurologic, or cognitive problems were divided equally into experimental (M = 63 years) and control groups (M = 65 years). Baseline and follow-up assessments for speech in noise (SNR50) and reverberation was carried out for both groups. The participants in the experimental group were subjected to Carnatic classical music training, which lasted for seven days. The Bayesian likelihood estimates revealed no difference in SNR50 and speech recognition scores in reverberation between baseline and followed-up assessment for the control group. Whereas, in the experimental group, the SNR50 reduced, and speech recognition scores improved following musical training, suggesting the positive impact of music training. The improved performance on speech recognition suggests that short-term musical training using Carnatic music can be used as a potential tool to improve speech recognition abilities in adverse listening conditions in older adults.
    Źródło:
    Archives of Acoustics; 2024, 49, 1; 3-9
    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 1-2 z 2

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