• Login
    View Item 
    •   DSpace Home
    • Institute's contributions
    • Scholarly literature
    • View Item
    •   DSpace Home
    • Institute's contributions
    • Scholarly literature
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Biodiversity assessment using passive acoustic recordings from off-reef location�Unsupervised learning to classify fish vocalization

    Thumbnail
    View/Open
    9706.pdf (2.267Mb)
    Date
    2023
    Author
    Mahale, V.
    Chanda, K.
    Chakraborty, B.
    Salkar, T.
    Sreekanth, G.B.
    Metadata
    Show full item record
    Abstract
    We present the quantitative characterization of Grande Island�s off-reef acoustic environment within the Zuari estuary during the pre-monsoon period. Passive acoustic recordings reveal prominent fish choruses. Detailed characteristics of the call employing oscillograms and individual fish call parameters of the segmented data include vocal groups such as Sciaenidae, Terapon theraps, and planktivorous as well as invertebrate sounds, e.g., snapping shrimp. We calculated biodiversity parameters (i) Acoustic Evenness Index (AEI), (ii) Acoustic Complexity Index (ACI), and mean sound pressure level (SPLrms) for three frequency bands such as full band (50-22?050?Hz), the low-frequency fish band (100-2000?Hz), and the high-frequency shrimp band (2000-20?000?Hz). Here, ACI and AEI metrics characterize the location�s soundscape data effectively indicating increased biodiversity of fish species for both the low-frequency and high-frequency bands. Whereas variations for SPLrms are prominent for three frequency bands. Moreover, we employ unsupervised classification through a hybrid technique comprising principal component analysis (PCA) and K-means clustering for data features of four fish sound types. Employed PCA for dimensionality reduction and related K-means clustering successfully provides 96.20%, 76.81%, 100.00%, and 86.36% classification during the dominant fish chorus. Overall, classification performance (89.84%) is helpful in the real-time monitoring of the fish stocks in the ecosystem
    URI
    https://doi.org/10.1121/10.0017248
    http://drs.nio.res.in/drs/handle/2264/9052
    Collections
    • Scholarly literature [9074]

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    DSpace software copyright © 2002-2016  DuraSpace
    Contact Us | Send Feedback
    Theme by 
    Atmire NV