Show simple item record

dc.contributor.authorMahale, V.
dc.contributor.authorChanda, K.
dc.contributor.authorChakraborty, B.
dc.contributor.authorSalkar, T.
dc.contributor.authorSreekanth, G.B.
dc.date.accessioned2023-04-12
dc.date.accessioned2023-07-17T15:32:52Z
dc.date.available2023-03-03
dc.date.available2023-07-17T15:32:52Z
dc.date.issued2023
dc.identifier.citationThe Journal of the Acoustical Society of America, vol.153(3); 2023; Article no: 1534
dc.identifier.issn0001-4966
dc.identifier.otherhttps://asa.scitation.org/doi/abs/10.1121/10.0017248
dc.identifier.urihttps://doi.org/10.1121/10.0017248
dc.identifier.urihttp://drs.nio.res.in/drs/handle/2264/9052
dc.description2.482
dc.description.abstractWe 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
dc.languageen
dc.publisherAcoustical Society of America (ASA)
dc.relation.isreferencedbySCI
dc.rightsOnly the author version of the copright reserved papers uploaded. Contact drs@nio.org for any complaints regarding copyright violations
dc.titleBiodiversity assessment using passive acoustic recordings from off-reef location�Unsupervised learning to classify fish vocalization
dc.typeJournal Article
dc.affiliation.authorCSIR-National Institute of Oceanography, Dona Paula, Goa, India


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record