New bridging eco-acoustic indices inspired by deep neural networks for fine-grained bird vocalization recognition across diurnal cycles.

Abstract

Revealing difference in bird vocalization changes from the perspectives of song recognition and acoustic indices has become a hot topic and challenge in recent ecological landscape research. This paper proposes a fine-grained (Dawn, noon, night) bird vocalization recognition framework based on a two-layer deep network to identify the same species' bird vocalization at different times of the day. Additionally, a new acoustic index method, the Log-Mel Acoustic Complexity Index (Log-Mel ACI), is introduced to explore the differences in bird vocalization of the same species throughout the day. The results of two-layer deep network showed significant separability of the bird vocalization of the same species at dawn, noon, and night based on Log-Mel spectrum. Furthermore, it was found that the improved ACI based on Log-Mel exhibits better circadian rhythmic performance than the traditional ACI, being highest at dawn, followed by night, and lowest at noon. These findings demonstrate that Log-Mel is effective in both deep network recognition and ACI calculation.