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    Wyświetlanie 1-3 z 3
    Tytuł:
    Brain’s Frequency Following Responses to Low-Frequency and Infrasound
    Autorzy:
    Jurado, Carlos
    Marquardt, Torsten
    Powiązania:
    https://bibliotekanauki.pl/articles/176507.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    low-frequency hearing
    frequency-following response
    infrasound
    auditory brain
    Opis:
    Complaints and awareness about environmental low-frequency (LF) noise and infrasound (IS) have increased in recent years, but knowledge about perceptual mechanisms is limited. To evaluate the use of the brain’s frequency-following response (FFR) as an objective correlate of individual sensitivity to IS and LF, we recorded the FFR to monaurally presented IS (11 Hz) and LF (38 Hz) tones over a 30-phon range for 11 subjects. It was found that 11-Hz FFRs were often significant already at ~0 phon, steeply grew to 20 phon, and saturated above. In contrast, the 38-Hz FFR growth was relatively shallow and continued to 60 phon. Furthermore, at the same loudness level (30 phon), the 11-Hz FFR strength was significantly larger (4.5 dB) than for 38 Hz, possibly reflecting a higher phase synchronization across the auditory pathway. Overall, unexpected inter-individual variability as well as qualitative differences between the measured FFR growth functions and typical loudness growth make interpretation of the FFR as objective correlate of IS and LF sensitivity difficult.
    Źródło:
    Archives of Acoustics; 2020, 45, 2; 313-319
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    VMD and CNN-Based Classification Model for Infrasound Signal
    Autorzy:
    Lu, Quanbo
    Li, Mei
    Powiązania:
    https://bibliotekanauki.pl/articles/31339812.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    infrasound signal
    variational mode decomposition
    convolutional neural network
    Fast Fourier Transform
    Opis:
    Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.
    Źródło:
    Archives of Acoustics; 2023, 48, 3; 403-412
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Infrasound Signal Classification Based on ICA and SVM
    Autorzy:
    Lu, Quanbo
    Wang, Meng
    Li, Mei
    Powiązania:
    https://bibliotekanauki.pl/articles/31339863.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    independent component analysis
    fast Fourier transform
    support vector machine
    infrasound signal
    Opis:
    A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.
    Źródło:
    Archives of Acoustics; 2023, 48, 3; 191-199
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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