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  • Comparative Studies. Non-European Languages/Literatures  (4)
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  • Comparative Studies. Non-European Languages/Literatures  (4)
RVK
  • 1
    Online Resource
    Online Resource
    Automated classification of Tursiops aduncus whistles based on a depth-wise separable convolutional neural network and data augmentation
    Acoustical Society of America (ASA) ; 2021
    In:  The Journal of the Acoustical Society of America Vol. 150, No. 5 ( 2021-11-01), p. 3861-3873
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 150, No. 5 ( 2021-11-01), p. 3861-3873
    Abstract: Whistle classification plays an essential role in studying the habitat and social behaviours of cetaceans. We obtained six categories of sweep whistles of two Tursiops aduncus individual signals using the passive acoustic mornitoring technique over a period of eight months in the Xiamen area. First, we propose a depthwise separable convolutional neural network for whistle classification. The proposed model adopts the depthwise convolution combined with the followed point-by-point convolution instead of the conventional convolution. As a result, it brings a better classification performance in sample sets with relatively independent features between different channels. Meanwhile, it leads to less computational complexity and fewer model parameters. Second, in order to solve the problem of an imbalance in the number of samples under each whistle category, we propose a random series method with five audio augmentation algorithms. The generalization ability of the trained model was improved by using an opening probability for each algorithm and the random selection of each augmentation factor within specific ranges. Finally, we explore the effect of the proposed augmentation method on the performance of our proposed architecture and find that it enhances the accuracy up to 98.53% for the classification of Tursiops aduncus whistles.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/10.0007291
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2021
    detail.hit.zdb_id: 1461063-2
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Robust unsupervised Tursiops aduncus whistle-event detection using gammatone multi-channel Savitzky–Golay based whistle enhancement
    Acoustical Society of America (ASA) ; 2022
    In:  The Journal of the Acoustical Society of America Vol. 151, No. 5 ( 2022-05-01), p. 3509-3521
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 151, No. 5 ( 2022-05-01), p. 3509-3521
    Abstract: Detecting whistle events is essential when studying the population density and behavior of cetaceans. After eight months of passive acoustic monitoring in Xiamen, we obtained long calls from two Tursiops aduncus individuals. In this paper, we propose an algorithm with an unbiased gammatone multi-channel Savitzky–Golay for smoothing dynamic continuous background noise and interference from long click trains. The algorithm uses the method of least squares to perform a local polynomial regression on the time–frequency representation of multi-frequency resolution call measurements, which can effectively retain the whistle profiles while filtering out noise and interference. We prove that it is better at separating out whistles and has lower computational complexity than other smoothing methods. In order to further extract whistle features in enhanced spectrograms, we also propose a set of multi-scale and multi-directional moving filter banks for various whistle durations and contour shapes. The final binary adaptive decisions at frame level for whistle events are obtained from the histograms of multi-scale and multi-directional spectrograms. Finally, we explore the entire data set and find that the proposed scheme achieves the highest frame-level F1-scores when detecting T. aduncus whistles than the baseline schemes, with an improvement of more than 6%.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/10.0011402
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2022
    detail.hit.zdb_id: 1461063-2
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  • 3
    Online Resource
    Online Resource
    Robust unsupervised Tursiops aduncus whistle enhancement based on complete ensembled empirical optimal envelope local mean decomposition with adaptive noise
    Acoustical Society of America (ASA) ; 2022
    In:  The Journal of the Acoustical Society of America Vol. 152, No. 6 ( 2022-12-01), p. 3360-3372
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 152, No. 6 ( 2022-12-01), p. 3360-3372
    Abstract: Whistle enhancement is an essential preprocessing step in studying dolphin behavior and population distributions. We propose a robust unsupervised whistle enhancement scheme based on improved local mean decomposition using adaptive noise estimation and logarithmic spectral amplitude. First, to further mitigate the mode aliasing problem effect in whistle signal decomposition and achieve better spectral separation of modes, we present a complete ensembled empirical optimal envelope local mean decomposition with adaptive noise algorithm. According to the envelope characteristics of the whistle signals, the proposed algorithm optimally and adaptively decomposes the noisy signal into product functions (PFs) with amplitude and frequency modulation. Second, the whistle enhancement framework consists of the improved minima-controlled recursive averaging for adaptive noise estimation, optimally modified log-spectral amplitude for each noisy product function enhancement, and the Hurst index for reconstructing pure whistle signal estimations with the least damaged PFs. Finally, the proposed scheme is applied to a dataset of long calls from two Tursiops aduncus individuals. After constructing the pure whistle dataset, the experimental results show that the proposed scheme performs better than other compared whistle enhancement schemes under different signal-to-noise ratios.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/10.0016500
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2022
    detail.hit.zdb_id: 1461063-2
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Automated classification of dolphin whistles based on the convolutional neural network
    Acoustical Society of America (ASA) ; 2019
    In:  The Journal of the Acoustical Society of America Vol. 146, No. 4_Supplement ( 2019-10-01), p. 2984-2984
    Details
    In: The Journal of the Acoustical Society of America, Acoustical Society of America (ASA), Vol. 146, No. 4_Supplement ( 2019-10-01), p. 2984-2984
    Abstract: A number of classification systems for dolphin whistles are applied to study the relation between dolphin whistles and their behaviors. Traditional approaches require prior knowledge, numerous pre-processing and manually extracting the features by conventional signal processing methods. In this study, deep convolutional neural networks are used to classify whistles and automatically learn the sound characteristics from training data with less pre-processing. The classification system is trained by using a database of 9 sorts of measured dolphin signals with nearly 4000 samples, and whistle contours in testing dataset are divided into six types, including constant frequency, upsweep, downsweep, concave, convex, and multiple. Finally, more than 90% for the classification accuracy rate is reached, and results show insensitivity to background noise. Therefore, the algorithm can be employed to study the potential relation between dolphin whistle signals and behaviors, and then facilitate future studies on dolphin habits.
    Type of Medium: Online Resource
    ISSN: 0001-4966 , 1520-8524
    URL: Article
    DOI: 10.1121/1.5137331
    RVK:
    EQ 1000
    Language: English
    Publisher: Acoustical Society of America (ASA)
    Publication Date: 2019
    detail.hit.zdb_id: 1461063-2
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    Online Resource
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