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

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.