Select Language

AI社区

公开数据集

AnuranCalls(MFCC)物种识别挑战数据集

AnuranCalls(MFCC)物种识别挑战数据集

1.24M
393 浏览
0 喜欢
1 次下载
0 条讨论
Animals Classification

Data Set Information:该数据集被用于几个与anuran物种识别挑战相关的分类任务中。它是一个包含三列标签的多标签数据集。这个数......

数据结构 ? 1.24M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Data Set Information:

    该数据集被用于几个与anuran物种识别挑战相关的分类任务中。它是一个包含三列标签的多标签数据集。这个数据集是通过分割60个音频记录创建的,这些音频记录属于4个不同的科、8个属和10个种。每个音频对应一个样本(单个青蛙),记录ID也包含在一个额外的列中。我们使用谱熵和二元聚类方法来检测属于每个音节的音频帧。在Matlab中进行了图像分割和特征提取。分割后得到7195个音节,作为训练和测试分类器的实例。这些记录是在真实噪声条件下(背景声)就地收集的。有些物种来自马瑙斯亚马逊大学的校园,还有马塔尔的其他大学。¢巴西恩蒂卡,其中一人来自阿根廷科尔多瓦。记录以wav格式存储,采样频率为44.1kHz,分辨率为32位,这使我们能够分析高达22kHz的信号。从每个提取的音节中,使用44个三角形滤波器计算22个MFCC。这些系数在-1 a‰mfcc a‰1之间标准化。每个类的实例数为:

    Families:
    Bufonidae              68
        Dendrobatidae         542
        Hylidae              2165
        Leptodactylidae      4420

    Genus:
        Adenomera          4150
        Ameerega            542
        Dendropsophus       310
        Hypsiboas          1593
        Leptodactylus       270
        Osteocephalus       114
        Rhinella             68
        Scinax              148

    Species:
        AdenomeraAndre             672
        AdenomeraHylaedacta€|       3478
        Ameeregatrivittata         542
        HylaMinuta                 310
        HypsiboasCinerascens       472
        HypsiboasCordobae         1121
        LeptodactylusFuscus        270
        OsteocephalusOophaa€|        114
        Rhinellagranulosa           68
        ScinaxRuber                148


    Attribute Information:

    Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an mel-frequency cepstrum (MFC). Due to each syllable has different length, every row (i) was normalized acording to MFCCs_i/(max(abs(MFCCs_i))).


    Relevant Papers:

    1) colonNA, J. G.; CRISTO, M.; SALVATIERRA, M.; NAKAMURA, E. F.
    An Incremental Technique for Real-Time Bioacoustic Signal Segmentation.
    Expert Systems with Applications, v. 42, p. 7367-7374, 2015.

    2) colonNA, J. G.; GAMA, J.; NAKAMURA, E. F.
    How to Correctly evaluate an Automatic Bioacoustics Classification Method.
    In: 17th Conference of the Spanish Association for Artificial Intelligence (CAEPIA).
    Lecture Notes in Computer Science. 986ed.: Springer International Publishing, 2016, v. , p. 37-47.

    3) colonNA, J. G.; GAMA, J.; NAKAMURA, E. F.
    Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach.
    Lecture Notes in Computer Science. 995ed.: Springer International Publishing, 2016, v. 9956, p. 198-212.

    4) colonNA, J. G.; RIBAS, A. D.; SANTOS, E. M.; NAKAMURA, E. F.
    Feature Subset Selection for Automatically Classifying Anuran Calls Using Sensor Networks.
    In: International Joint Conference on Neural Networks, 2012, Brisbane.
    Proceedings of the International Joint Conference on Neural Networks (IJCNN 2012), 2012. p. 1-8. IEEE

    5) colonNA, J. G.; PEET, T.; FERREIRA, C. A.; JORGE, A. M.; GOMES, E. F.; GAMA, J. (2016, July).
    Automatic Classification of Anuran Sounds Using Convolutional Neural Networks.
    In Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering (No. C3S2E '16, pp. 73-78). ACM.

    6) colonNA, J. G.; CRISTO, M.; NAKAMURA, E. F. (2014, August).
    A Distributed Approach for Classifying Anuran Species based on Their Calls.
    In Pattern Recognition (ICPR), 2014 22nd International Conference on (pp. 1242-1247). IEEE.

    7) RIBAS, A. D.; colonNA, J. G.; FIGUEIREDO, C. M. S.; NAKAMURA, E. F.
    Similarity clustering for data fusion in wireless sensor networks using k-means
    The 2012 International Joint Conference on Neural Networks (IJCNN 2012), p. 1-7. IEEE

    8) DIAZ, J. M.; colonNA, J. G.; SOARES, R. B.; FIGUEREIDO, C. M. S.; NAKAMURA, E. F.
    Compressive sensing for efficiently collecting wildlife sounds with wireless sensor networks
    21st International Conference on Computer Communications and Networks (ICCCN 2012), p. 1-7. IEEE


    Citation Request:

    Please refer to the Machine Learning Repository's citation policy

    ×

    帕依提提提温馨提示

    该数据集正在整理中,为您准备了其他渠道,请您使用

    注:部分数据正在处理中,未能直接提供下载,还请大家理解和支持。
    暂无相关内容。
    暂无相关内容。
    • 分享你的想法
    去分享你的想法~~

    全部内容

      欢迎交流分享
      开始分享您的观点和意见,和大家一起交流分享.
    所需积分:15 去赚积分?
    • 393浏览
    • 1下载
    • 0点赞
    • 收藏
    • 分享