Complexity-entropy based approach for detection of fish choruses

Increasing anthropogenic noise around the world ocean are affecting the marine ecology. Recently, acoustic indices (AI) were utilized to quantify the biophony in the marine soundscape. However, these AI’s employed in complex marine environment, dominated by several anthropogenic and geophonic sources are yet to be understood. In this study, we have introduced a method based on complexity-entropy (C-H) for detection of biophonic sounds originating from fish chorus. The fish chorus detection performance of C-H was compared with AI’s such as acoustic complexity index (ACI), acoustic diversity index (ADI), and bioacoustics index (BI). We have utilized the data collected at Changhua (A1) and Miaoli (N1). During the Spring of 2016 and 2017, the region N1 was exposed to continual shipping activities, due to which there was ~10 dB increase in the low frequency (5–500 Hz) noise levels. This enabled us to evaluate the fish chorus detection performance of various AI’s and C-H method, and the robustness in the presence and absence of shipping activities. The results presented in this study shows that, during the fish chorusing hours, the introduced entropy is positively correlated with Pearson’s correlation coefficient (P cc) > 0.95 and complexity is anticorrelated with P cc < -0.95. Therefore, the introduced C-H method has potential implication in efficient detection of fish chorus and overcome the limitations confronted by AI’s such as ACI, ADI, and BI.